import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
from collections import OrderedDict
from torch import nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 768
    xnumel = 49
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 49 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 147 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 12
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = yindex // 512
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 512 * x2 + 4608 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 1024
    y1 = yindex // 1024
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 1024 * x2 + 9216 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 2048
    y1 = yindex // 2048
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 2048 * x2 + 18432 * y1), tmp0, xmask)


@triton.jit
def triton_per_fused_add_div_sqrt_sub_var_mean_6(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    rnumel = 147
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 147 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(rmask & xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 147, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(rmask & xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 147.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-10
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.sqrt(tmp20)
    tmp22 = tmp0 - tmp10
    tmp23 = tmp22 / tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(out_ptr1 + (r1 + 147 * x0), tmp23, rmask & xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_7(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex // 8704 % 34
    x1 = xindex // 256 % 34
    x3 = xindex // 295936
    x4 = xindex % 8704
    x6 = xindex
    tmp0 = -1 + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-8448 + x4 + 8192 * x2 + 262144 * x3), tmp10,
        other=0.0)
    tl.store(out_ptr0 + x6, tmp11, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_8(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 256
    x1 = xindex // 256 % 16
    x2 = xindex // 4096 % 16
    x3 = xindex // 65536
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 512 * x1 + 17408 * x2 + 295936 * x3), None)
    tmp1 = tl.load(in_ptr0 + (256 + x0 + 512 * x1 + 17408 * x2 + 295936 *
        x3), None)
    tmp3 = tl.load(in_ptr0 + (512 + x0 + 512 * x1 + 17408 * x2 + 295936 *
        x3), None)
    tmp5 = tl.load(in_ptr0 + (8704 + x0 + 512 * x1 + 17408 * x2 + 295936 *
        x3), None)
    tmp7 = tl.load(in_ptr0 + (8960 + x0 + 512 * x1 + 17408 * x2 + 295936 *
        x3), None)
    tmp9 = tl.load(in_ptr0 + (9216 + x0 + 512 * x1 + 17408 * x2 + 295936 *
        x3), None)
    tmp11 = tl.load(in_ptr0 + (17408 + x0 + 512 * x1 + 17408 * x2 + 295936 *
        x3), None)
    tmp13 = tl.load(in_ptr0 + (17664 + x0 + 512 * x1 + 17408 * x2 + 295936 *
        x3), None)
    tmp15 = tl.load(in_ptr0 + (17920 + x0 + 512 * x1 + 17408 * x2 + 295936 *
        x3), None)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tmp1 > tmp0
    tmp18 = tl.full([1], 1, tl.int8)
    tmp19 = tl.full([1], 0, tl.int8)
    tmp20 = tl.where(tmp17, tmp18, tmp19)
    tmp21 = tmp3 > tmp2
    tmp22 = tl.full([1], 2, tl.int8)
    tmp23 = tl.where(tmp21, tmp22, tmp20)
    tmp24 = tmp5 > tmp4
    tmp25 = tl.full([1], 3, tl.int8)
    tmp26 = tl.where(tmp24, tmp25, tmp23)
    tmp27 = tmp7 > tmp6
    tmp28 = tl.full([1], 4, tl.int8)
    tmp29 = tl.where(tmp27, tmp28, tmp26)
    tmp30 = tmp9 > tmp8
    tmp31 = tl.full([1], 5, tl.int8)
    tmp32 = tl.where(tmp30, tmp31, tmp29)
    tmp33 = tmp11 > tmp10
    tmp34 = tl.full([1], 6, tl.int8)
    tmp35 = tl.where(tmp33, tmp34, tmp32)
    tmp36 = tmp13 > tmp12
    tmp37 = tl.full([1], 7, tl.int8)
    tmp38 = tl.where(tmp36, tmp37, tmp35)
    tmp39 = tmp15 > tmp14
    tmp40 = tl.full([1], 8, tl.int8)
    tmp41 = tl.where(tmp39, tmp40, tmp38)
    tl.store(out_ptr0 + x4, tmp16, None)
    tl.store(out_ptr1 + x4, tmp41, None)


@triton.jit
def triton_red_fused_native_group_norm_9(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 32
    x1 = xindex // 32
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    x4 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex % 8
        r3 = rindex // 8
        tmp0 = tl.load(in_ptr0 + (r2 + 8 * x0 + 256 * r3 + 65536 * x1), 
            rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tl.store(out_ptr0 + x4, tmp2, xmask)
    tl.store(out_ptr1 + x4, tmp3, xmask)
    tmp5 = 2048.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-05
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.rsqrt(tmp8)
    tl.store(out_ptr2 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_relu_10(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 256
    x2 = xindex // 65536
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 8), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 8), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 2048.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_per_fused_add_div_sqrt_sub_var_mean_11(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 256 * x0), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 256, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-10
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.sqrt(tmp17)
    tmp19 = tmp0 - tmp8
    tmp20 = tmp19 / tmp18
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp18, None)
    tl.store(out_ptr1 + (r1 + 256 * x0), tmp20, None)


@triton.jit
def triton_per_fused_add_div_sqrt_sub_var_mean_12(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 256 * x0), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 256, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-10
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.sqrt(tmp17)
    tmp19 = tmp0 - tmp8
    tmp20 = tmp19 / tmp18
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp18, None)
    tl.store(out_ptr1 + (r1 + 256 * x0), tmp20, None)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_13(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 256
    rnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 2304 * x0), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 2304.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, xmask)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 2304 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 2304 * x0), tmp12, rmask & xmask)


@triton.jit
def triton_poi_fused_add_14(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = tl.load(in_out_ptr0 + x0, None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_red_fused_native_group_norm_15(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 32
    x1 = xindex // 32
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    x4 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex % 32
        r3 = rindex // 32
        tmp0 = tl.load(in_ptr0 + (r2 + 32 * x0 + 1024 * r3 + 262144 * x1), 
            rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tl.store(out_ptr0 + x4, tmp2, xmask)
    tl.store(out_ptr1 + x4, tmp3, xmask)
    tmp5 = 8192.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-05
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.rsqrt(tmp8)
    tl.store(out_ptr2 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_relu_16(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 1024
    x2 = xindex // 262144
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 32), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 32), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 8192.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_per_fused_add_div_sqrt_sub_var_mean_17(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 1024 * x0), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 1024, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 1024.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-10
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.sqrt(tmp17)
    tmp19 = tmp0 - tmp8
    tmp20 = tmp19 / tmp18
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp18, None)
    tl.store(out_ptr1 + (r1 + 1024 * x0), tmp20, None)


@triton.jit
def triton_poi_fused_add_18(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + x0, None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_per_fused_add_div_sqrt_sub_var_mean_19(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 1024 * x0), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 1024, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 1024.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-10
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.sqrt(tmp17)
    tmp19 = tmp0 - tmp8
    tmp20 = tmp19 / tmp18
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp18, None)
    tl.store(out_ptr1 + (r1 + 1024 * x0), tmp20, None)


@triton.jit
def triton_per_fused_add_div_sqrt_sub_var_mean_20(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 1024 * x0), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 1024, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 1024.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-10
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.sqrt(tmp17)
    tmp19 = tmp0 - tmp8
    tmp20 = tmp19 / tmp18
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp18, None)
    tl.store(out_ptr1 + (r1 + 1024 * x0), tmp20, None)


@triton.jit
def triton_red_fused_native_group_norm_21(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 32
    x1 = xindex // 32
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    x4 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex % 16
        r3 = rindex // 16
        tmp0 = tl.load(in_ptr0 + (r2 + 16 * x0 + 512 * r3 + 131072 * x1), 
            rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tl.store(out_ptr0 + x4, tmp2, xmask)
    tl.store(out_ptr1 + x4, tmp3, xmask)
    tmp5 = 4096.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-05
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.rsqrt(tmp8)
    tl.store(out_ptr2 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_relu_22(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 512
    x2 = xindex // 131072
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 16), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 16), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 4096.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_23(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 512
    rnumel = 4608
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 4608 * x0), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 4608.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, xmask)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 4608 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 4608 * x0), tmp12, rmask & xmask)


@triton.jit
def triton_per_fused_native_group_norm_24(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex % 16
    r3 = rindex // 16
    x0 = xindex % 32
    x1 = xindex // 32
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 16 * x0 + 512 * r3 + 32768 * x1), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 1024, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 1024.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tl.store(out_ptr2 + x4, tmp18, None)
    tl.store(out_ptr0 + x4, tmp8, None)
    tl.store(out_ptr1 + x4, tmp13, None)


@triton.jit
def triton_poi_fused_native_group_norm_relu_25(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 512
    x2 = xindex // 32768
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 16), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 16), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1024.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_per_fused_add_div_sqrt_sub_var_mean_26(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 512 * x0), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 512, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 512.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-10
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.sqrt(tmp17)
    tmp19 = tmp0 - tmp8
    tmp20 = tmp19 / tmp18
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp18, None)
    tl.store(out_ptr1 + (r1 + 512 * x0), tmp20, None)


@triton.jit
def triton_poi_fused_add_27(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = tl.load(in_out_ptr0 + x0, None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_red_fused_native_group_norm_28(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 32
    x1 = xindex // 32
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    x4 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex % 64
        r3 = rindex // 64
        tmp0 = tl.load(in_ptr0 + (r2 + 64 * x0 + 2048 * r3 + 131072 * x1), 
            rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tl.store(out_ptr0 + x4, tmp2, xmask)
    tl.store(out_ptr1 + x4, tmp3, xmask)
    tmp5 = 4096.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-05
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.rsqrt(tmp8)
    tl.store(out_ptr2 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_relu_29(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 2048
    x2 = xindex // 131072
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 64), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 64), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 4096.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_30(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 512
    rnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 2048 * x0), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 2048.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, xmask)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 2048 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 2048 * x0), tmp12, rmask & xmask)


@triton.jit
def triton_poi_fused_add_31(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + x0, None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_32(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 2048 * x0), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 2048.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, None)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 2048 * x0), rmask, eviction_policy=
            'evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 2048 * x0), tmp12, rmask)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_33(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 1024
    rnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 2048 * x0), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 2048.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, xmask)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 2048 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 2048 * x0), tmp12, rmask & xmask)


@triton.jit
def triton_red_fused_native_group_norm_34(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 32
    x1 = xindex // 32
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    x4 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex % 32
        r3 = rindex // 32
        tmp0 = tl.load(in_ptr0 + (r2 + 32 * x0 + 1024 * r3 + 65536 * x1), 
            rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tl.store(out_ptr0 + x4, tmp2, xmask)
    tl.store(out_ptr1 + x4, tmp3, xmask)
    tmp5 = 2048.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-05
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.rsqrt(tmp8)
    tl.store(out_ptr2 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_relu_35(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 1024
    x2 = xindex // 65536
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 32), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 32), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 2048.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_36(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 1024
    rnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 9216 * x0), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 9216.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, xmask)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 9216 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 9216 * x0), tmp12, rmask & xmask)


@triton.jit
def triton_per_fused_native_group_norm_37(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex % 32
    r3 = rindex // 32
    x0 = xindex % 32
    x1 = xindex // 32
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 32 * x0 + 1024 * r3 + 16384 * x1), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 512, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 512.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tl.store(out_ptr2 + x4, tmp18, None)
    tl.store(out_ptr0 + x4, tmp8, None)
    tl.store(out_ptr1 + x4, tmp13, None)


@triton.jit
def triton_poi_fused_native_group_norm_relu_38(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 1024
    x2 = xindex // 16384
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 32), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 32), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 512.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_per_fused_add_div_sqrt_sub_var_mean_39(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 1024 * x0), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 1024, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 1024.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-10
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.sqrt(tmp17)
    tmp19 = tmp0 - tmp8
    tmp20 = tmp19 / tmp18
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp18, None)
    tl.store(out_ptr1 + (r1 + 1024 * x0), tmp20, None)


@triton.jit
def triton_poi_fused_add_40(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = tl.load(in_out_ptr0 + x0, None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_red_fused_native_group_norm_41(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 32
    x1 = xindex // 32
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    x4 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex % 128
        r3 = rindex // 128
        tmp0 = tl.load(in_ptr0 + (r2 + 128 * x0 + 4096 * r3 + 65536 * x1), 
            rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tl.store(out_ptr0 + x4, tmp2, xmask)
    tl.store(out_ptr1 + x4, tmp3, xmask)
    tmp5 = 2048.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-05
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.rsqrt(tmp8)
    tl.store(out_ptr2 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_relu_42(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 4096
    x2 = xindex // 65536
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 128), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 128), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 2048.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_43(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 1024
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 4096.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, xmask)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 4096 * x0), tmp12, rmask & xmask)


@triton.jit
def triton_poi_fused_add_44(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + x0, None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_45(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 4096.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, None)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask, eviction_policy=
            'evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 4096 * x0), tmp12, rmask)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_46(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 4096.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, None)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 4096 * x0), rmask, eviction_policy=
            'evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 4096 * x0), tmp12, rmask)


@triton.jit
def triton_per_fused_native_group_norm_47(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex % 64
    r3 = rindex // 64
    x0 = xindex % 32
    x1 = xindex // 32
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 64 * x0 + 2048 * r3 + 32768 * x1), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 1024, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 1024.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tl.store(out_ptr2 + x4, tmp18, None)
    tl.store(out_ptr0 + x4, tmp8, None)
    tl.store(out_ptr1 + x4, tmp13, None)


@triton.jit
def triton_poi_fused_native_group_norm_relu_48(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 2048
    x2 = xindex // 32768
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 64), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 64), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1024.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_49(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 18432
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 18432 * x0), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 18432.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, None)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 18432 * x0), rmask, eviction_policy
            ='evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 18432 * x0), tmp12, rmask)


@triton.jit
def triton_per_fused_native_group_norm_50(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex % 64
    r3 = rindex // 64
    x0 = xindex % 32
    x1 = xindex // 32
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 64 * x0 + 2048 * r3 + 8192 * x1), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 256, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tl.store(out_ptr2 + x4, tmp18, None)
    tl.store(out_ptr0 + x4, tmp8, None)
    tl.store(out_ptr1 + x4, tmp13, None)


@triton.jit
def triton_poi_fused_native_group_norm_relu_51(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 2048
    x2 = xindex // 8192
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 64), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 64), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 256.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_52(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 2048 * x0), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 2048.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, None)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 2048 * x0), rmask, eviction_policy=
            'evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 2048 * x0), tmp12, rmask)


@triton.jit
def triton_poi_fused_add_53(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = tl.load(in_out_ptr0 + x0, None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_per_fused_native_group_norm_54(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex % 256
    r3 = rindex // 256
    x0 = xindex % 32
    x1 = xindex // 32
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 256 * x0 + 8192 * r3 + 32768 * x1), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 1024, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 1024.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tl.store(out_ptr2 + x4, tmp18, None)
    tl.store(out_ptr0 + x4, tmp8, None)
    tl.store(out_ptr1 + x4, tmp13, None)


@triton.jit
def triton_poi_fused_native_group_norm_relu_55(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 8192
    x2 = xindex // 32768
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 256), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 256), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1024.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_red_fused_add_div_sqrt_sub_var_mean_56(in_out_ptr0, in_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + 8192 * x0), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tmp5 = 8192.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-10
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp9, None)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp10 = tl.load(in_ptr0 + (r1 + 8192 * x0), rmask, eviction_policy=
            'evict_first', other=0.0)
        tmp11 = tmp10 - tmp2
        tmp12 = tmp11 / tmp9
        tl.store(out_ptr1 + (r1 + 8192 * x0), tmp12, rmask)


@triton.jit
def triton_poi_fused_add_57(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + x0, None)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_per_fused_native_group_norm_58(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex % 256
    r3 = rindex // 256
    x0 = xindex % 32
    x1 = xindex // 32
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 256 * x0 + 8192 * r3 + 32768 * x1), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 1024, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 1024.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x4, tmp18, None)
    tl.store(out_ptr0 + x4, tmp8, None)


@triton.jit
def triton_poi_fused_mean_native_group_norm_relu_59(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8192
    x1 = xindex // 8192
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 32768 * x1), None)
    tmp1 = tl.load(in_ptr1 + x2 // 256, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2 // 256, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (8192 + x0 + 32768 * x1), None)
    tmp18 = tl.load(in_ptr0 + (16384 + x0 + 32768 * x1), None)
    tmp25 = tl.load(in_ptr0 + (24576 + x0 + 32768 * x1), None)
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = tmp11 - tmp1
    tmp13 = tmp12 * tmp3
    tmp14 = tmp13 * tmp5
    tmp15 = tmp14 + tmp7
    tmp16 = triton_helpers.maximum(tmp9, tmp15)
    tmp17 = tmp10 + tmp16
    tmp19 = tmp18 - tmp1
    tmp20 = tmp19 * tmp3
    tmp21 = tmp20 * tmp5
    tmp22 = tmp21 + tmp7
    tmp23 = triton_helpers.maximum(tmp9, tmp22)
    tmp24 = tmp17 + tmp23
    tmp26 = tmp25 - tmp1
    tmp27 = tmp26 * tmp3
    tmp28 = tmp27 * tmp5
    tmp29 = tmp28 + tmp7
    tmp30 = triton_helpers.maximum(tmp9, tmp29)
    tmp31 = tmp24 + tmp30
    tmp32 = 4.0
    tmp33 = tmp31 / tmp32
    tl.store(out_ptr0 + x2, tmp33, None)


@triton.jit
def triton_poi_fused_convolution_60(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 87372
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 21843
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47,
        primals_48, primals_49, primals_50, primals_51, primals_52,
        primals_53, primals_54, primals_55, primals_56, primals_57,
        primals_58, primals_59, primals_60, primals_61, primals_62,
        primals_63, primals_64, primals_65, primals_66, primals_67,
        primals_68, primals_69, primals_70, primals_71, primals_72,
        primals_73, primals_74, primals_75, primals_76, primals_77,
        primals_78, primals_79, primals_80, primals_81, primals_82,
        primals_83, primals_84, primals_85, primals_86, primals_87,
        primals_88, primals_89, primals_90, primals_91, primals_92,
        primals_93, primals_94, primals_95, primals_96, primals_97,
        primals_98, primals_99, primals_100, primals_101, primals_102,
        primals_103, primals_104, primals_105, primals_106, primals_107,
        primals_108, primals_109, primals_110, primals_111, primals_112,
        primals_113, primals_114, primals_115, primals_116, primals_117,
        primals_118, primals_119, primals_120, primals_121, primals_122,
        primals_123, primals_124, primals_125, primals_126, primals_127,
        primals_128, primals_129, primals_130, primals_131, primals_132,
        primals_133, primals_134, primals_135, primals_136, primals_137,
        primals_138, primals_139, primals_140, primals_141, primals_142,
        primals_143, primals_144, primals_145, primals_146, primals_147,
        primals_148, primals_149, primals_150, primals_151, primals_152,
        primals_153, primals_154) = args
    args.clear()
    assert_size_stride(primals_1, (256, 3, 7, 7), (147, 49, 7, 1))
    assert_size_stride(primals_2, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_3, (256,), (1,))
    assert_size_stride(primals_4, (256,), (1,))
    assert_size_stride(primals_5, (1024, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_6, (256, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (256,), (1,))
    assert_size_stride(primals_9, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_10, (256,), (1,))
    assert_size_stride(primals_11, (256,), (1,))
    assert_size_stride(primals_12, (1024, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_13, (1024,), (1,))
    assert_size_stride(primals_14, (1024,), (1,))
    assert_size_stride(primals_15, (256, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_16, (256,), (1,))
    assert_size_stride(primals_17, (256,), (1,))
    assert_size_stride(primals_18, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_19, (256,), (1,))
    assert_size_stride(primals_20, (256,), (1,))
    assert_size_stride(primals_21, (1024, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_22, (1024,), (1,))
    assert_size_stride(primals_23, (1024,), (1,))
    assert_size_stride(primals_24, (256, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_25, (256,), (1,))
    assert_size_stride(primals_26, (256,), (1,))
    assert_size_stride(primals_27, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_28, (256,), (1,))
    assert_size_stride(primals_29, (256,), (1,))
    assert_size_stride(primals_30, (1024, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_31, (1024,), (1,))
    assert_size_stride(primals_32, (1024,), (1,))
    assert_size_stride(primals_33, (256, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_34, (256,), (1,))
    assert_size_stride(primals_35, (256,), (1,))
    assert_size_stride(primals_36, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_37, (256,), (1,))
    assert_size_stride(primals_38, (256,), (1,))
    assert_size_stride(primals_39, (1024, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_40, (1024,), (1,))
    assert_size_stride(primals_41, (1024,), (1,))
    assert_size_stride(primals_42, (2048, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_43, (512, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_44, (512,), (1,))
    assert_size_stride(primals_45, (512,), (1,))
    assert_size_stride(primals_46, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_47, (512,), (1,))
    assert_size_stride(primals_48, (512,), (1,))
    assert_size_stride(primals_49, (2048, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_50, (2048,), (1,))
    assert_size_stride(primals_51, (2048,), (1,))
    assert_size_stride(primals_52, (512, 2048, 1, 1), (2048, 1, 1, 1))
    assert_size_stride(primals_53, (512,), (1,))
    assert_size_stride(primals_54, (512,), (1,))
    assert_size_stride(primals_55, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_56, (512,), (1,))
    assert_size_stride(primals_57, (512,), (1,))
    assert_size_stride(primals_58, (2048, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_59, (2048,), (1,))
    assert_size_stride(primals_60, (2048,), (1,))
    assert_size_stride(primals_61, (512, 2048, 1, 1), (2048, 1, 1, 1))
    assert_size_stride(primals_62, (512,), (1,))
    assert_size_stride(primals_63, (512,), (1,))
    assert_size_stride(primals_64, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_65, (512,), (1,))
    assert_size_stride(primals_66, (512,), (1,))
    assert_size_stride(primals_67, (2048, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_68, (2048,), (1,))
    assert_size_stride(primals_69, (2048,), (1,))
    assert_size_stride(primals_70, (512, 2048, 1, 1), (2048, 1, 1, 1))
    assert_size_stride(primals_71, (512,), (1,))
    assert_size_stride(primals_72, (512,), (1,))
    assert_size_stride(primals_73, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_74, (512,), (1,))
    assert_size_stride(primals_75, (512,), (1,))
    assert_size_stride(primals_76, (2048, 512, 1, 1), (512, 1, 1, 1))
    assert_size_stride(primals_77, (2048,), (1,))
    assert_size_stride(primals_78, (2048,), (1,))
    assert_size_stride(primals_79, (4096, 2048, 1, 1), (2048, 1, 1, 1))
    assert_size_stride(primals_80, (1024, 2048, 1, 1), (2048, 1, 1, 1))
    assert_size_stride(primals_81, (1024,), (1,))
    assert_size_stride(primals_82, (1024,), (1,))
    assert_size_stride(primals_83, (1024, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_84, (1024,), (1,))
    assert_size_stride(primals_85, (1024,), (1,))
    assert_size_stride(primals_86, (4096, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_87, (4096,), (1,))
    assert_size_stride(primals_88, (4096,), (1,))
    assert_size_stride(primals_89, (1024, 4096, 1, 1), (4096, 1, 1, 1))
    assert_size_stride(primals_90, (1024,), (1,))
    assert_size_stride(primals_91, (1024,), (1,))
    assert_size_stride(primals_92, (1024, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_93, (1024,), (1,))
    assert_size_stride(primals_94, (1024,), (1,))
    assert_size_stride(primals_95, (4096, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_96, (4096,), (1,))
    assert_size_stride(primals_97, (4096,), (1,))
    assert_size_stride(primals_98, (1024, 4096, 1, 1), (4096, 1, 1, 1))
    assert_size_stride(primals_99, (1024,), (1,))
    assert_size_stride(primals_100, (1024,), (1,))
    assert_size_stride(primals_101, (1024, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_102, (1024,), (1,))
    assert_size_stride(primals_103, (1024,), (1,))
    assert_size_stride(primals_104, (4096, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_105, (4096,), (1,))
    assert_size_stride(primals_106, (4096,), (1,))
    assert_size_stride(primals_107, (1024, 4096, 1, 1), (4096, 1, 1, 1))
    assert_size_stride(primals_108, (1024,), (1,))
    assert_size_stride(primals_109, (1024,), (1,))
    assert_size_stride(primals_110, (1024, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_111, (1024,), (1,))
    assert_size_stride(primals_112, (1024,), (1,))
    assert_size_stride(primals_113, (4096, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_114, (4096,), (1,))
    assert_size_stride(primals_115, (4096,), (1,))
    assert_size_stride(primals_116, (8192, 4096, 1, 1), (4096, 1, 1, 1))
    assert_size_stride(primals_117, (2048, 4096, 1, 1), (4096, 1, 1, 1))
    assert_size_stride(primals_118, (2048,), (1,))
    assert_size_stride(primals_119, (2048,), (1,))
    assert_size_stride(primals_120, (2048, 2048, 3, 3), (18432, 9, 3, 1))
    assert_size_stride(primals_121, (2048,), (1,))
    assert_size_stride(primals_122, (2048,), (1,))
    assert_size_stride(primals_123, (8192, 2048, 1, 1), (2048, 1, 1, 1))
    assert_size_stride(primals_124, (8192,), (1,))
    assert_size_stride(primals_125, (8192,), (1,))
    assert_size_stride(primals_126, (2048, 8192, 1, 1), (8192, 1, 1, 1))
    assert_size_stride(primals_127, (2048,), (1,))
    assert_size_stride(primals_128, (2048,), (1,))
    assert_size_stride(primals_129, (2048, 2048, 3, 3), (18432, 9, 3, 1))
    assert_size_stride(primals_130, (2048,), (1,))
    assert_size_stride(primals_131, (2048,), (1,))
    assert_size_stride(primals_132, (8192, 2048, 1, 1), (2048, 1, 1, 1))
    assert_size_stride(primals_133, (8192,), (1,))
    assert_size_stride(primals_134, (8192,), (1,))
    assert_size_stride(primals_135, (2048, 8192, 1, 1), (8192, 1, 1, 1))
    assert_size_stride(primals_136, (2048,), (1,))
    assert_size_stride(primals_137, (2048,), (1,))
    assert_size_stride(primals_138, (2048, 2048, 3, 3), (18432, 9, 3, 1))
    assert_size_stride(primals_139, (2048,), (1,))
    assert_size_stride(primals_140, (2048,), (1,))
    assert_size_stride(primals_141, (8192, 2048, 1, 1), (2048, 1, 1, 1))
    assert_size_stride(primals_142, (8192,), (1,))
    assert_size_stride(primals_143, (8192,), (1,))
    assert_size_stride(primals_144, (2048, 8192, 1, 1), (8192, 1, 1, 1))
    assert_size_stride(primals_145, (2048,), (1,))
    assert_size_stride(primals_146, (2048,), (1,))
    assert_size_stride(primals_147, (2048, 2048, 3, 3), (18432, 9, 3, 1))
    assert_size_stride(primals_148, (2048,), (1,))
    assert_size_stride(primals_149, (2048,), (1,))
    assert_size_stride(primals_150, (8192, 2048, 1, 1), (2048, 1, 1, 1))
    assert_size_stride(primals_151, (8192,), (1,))
    assert_size_stride(primals_152, (8192,), (1,))
    assert_size_stride(primals_153, (21843, 8192, 1, 1), (8192, 1, 1, 1))
    assert_size_stride(primals_154, (21843,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((256, 3, 7, 7), (147, 1, 21, 3), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(768, 49)](primals_1, buf0, 768, 49, XBLOCK=
            32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
            .float32)
        triton_poi_fused_1[grid(12, 4096)](primals_2, buf1, 12, 4096,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_2[grid(65536, 9)](primals_9, buf2, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_9
        buf3 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_2[grid(65536, 9)](primals_18, buf3, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_18
        buf4 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_2[grid(65536, 9)](primals_27, buf4, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_27
        buf5 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_2[grid(65536, 9)](primals_36, buf5, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_36
        buf6 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_3[grid(262144, 9)](primals_46, buf6, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_46
        buf7 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_3[grid(262144, 9)](primals_55, buf7, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_55
        buf8 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_3[grid(262144, 9)](primals_64, buf8, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_64
        buf9 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_3[grid(262144, 9)](primals_73, buf9, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_73
        buf10 = empty_strided_cuda((1024, 1024, 3, 3), (9216, 1, 3072, 1024
            ), torch.float32)
        triton_poi_fused_4[grid(1048576, 9)](primals_83, buf10, 1048576, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_83
        buf11 = empty_strided_cuda((1024, 1024, 3, 3), (9216, 1, 3072, 1024
            ), torch.float32)
        triton_poi_fused_4[grid(1048576, 9)](primals_92, buf11, 1048576, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_92
        buf12 = empty_strided_cuda((1024, 1024, 3, 3), (9216, 1, 3072, 1024
            ), torch.float32)
        triton_poi_fused_4[grid(1048576, 9)](primals_101, buf12, 1048576, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_101
        buf13 = empty_strided_cuda((1024, 1024, 3, 3), (9216, 1, 3072, 1024
            ), torch.float32)
        triton_poi_fused_4[grid(1048576, 9)](primals_110, buf13, 1048576, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_110
        buf14 = empty_strided_cuda((2048, 2048, 3, 3), (18432, 1, 6144, 
            2048), torch.float32)
        triton_poi_fused_5[grid(4194304, 9)](primals_120, buf14, 4194304, 9,
            XBLOCK=16, YBLOCK=128, num_warps=8, num_stages=1)
        del primals_120
        buf15 = empty_strided_cuda((2048, 2048, 3, 3), (18432, 1, 6144, 
            2048), torch.float32)
        triton_poi_fused_5[grid(4194304, 9)](primals_129, buf15, 4194304, 9,
            XBLOCK=16, YBLOCK=128, num_warps=8, num_stages=1)
        del primals_129
        buf16 = empty_strided_cuda((2048, 2048, 3, 3), (18432, 1, 6144, 
            2048), torch.float32)
        triton_poi_fused_5[grid(4194304, 9)](primals_138, buf16, 4194304, 9,
            XBLOCK=16, YBLOCK=128, num_warps=8, num_stages=1)
        del primals_138
        buf17 = empty_strided_cuda((2048, 2048, 3, 3), (18432, 1, 6144, 
            2048), torch.float32)
        triton_poi_fused_5[grid(4194304, 9)](primals_147, buf17, 4194304, 9,
            XBLOCK=16, YBLOCK=128, num_warps=8, num_stages=1)
        del primals_147
        buf19 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf21 = reinterpret_tensor(buf19, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf19
        buf22 = empty_strided_cuda((256, 3, 7, 7), (147, 1, 21, 3), torch.
            float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_6[grid(256)](buf21, buf0,
            buf22, 256, 147, XBLOCK=1, num_warps=2, num_stages=1)
        buf23 = extern_kernels.convolution(buf1, buf22, stride=(2, 2),
            padding=(3, 3), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf23, (4, 256, 32, 32), (262144, 1, 8192, 256))
        buf24 = empty_strided_cuda((4, 256, 34, 34), (295936, 1, 8704, 256),
            torch.float32)
        triton_poi_fused_constant_pad_nd_7[grid(1183744)](buf23, buf24, 
            1183744, XBLOCK=1024, num_warps=4, num_stages=1)
        buf25 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        buf26 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_8[grid(262144)](buf24,
            buf25, buf26, 262144, XBLOCK=512, num_warps=8, num_stages=1)
        buf27 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf28 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf30 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf25, buf27, buf28,
            buf30, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf31 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf25,
            buf27, buf28, primals_3, primals_4, buf31, 262144, XBLOCK=1024,
            num_warps=4, num_stages=1)
        del primals_4
        buf33 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf35 = reinterpret_tensor(buf33, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf33
        buf36 = empty_strided_cuda((1024, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_11[grid(1024)](buf35,
            primals_5, buf36, 1024, 256, num_warps=2, num_stages=1)
        buf37 = extern_kernels.convolution(buf31, buf36, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf37, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf39 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf41 = reinterpret_tensor(buf39, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf39
        buf42 = empty_strided_cuda((256, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_12[grid(256)](buf41,
            primals_6, buf42, 256, 256, num_warps=2, num_stages=1)
        buf43 = extern_kernels.convolution(buf31, buf42, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf43, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf44 = buf28
        del buf28
        buf45 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf47 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf43, buf44, buf45,
            buf47, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf48 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf43,
            buf44, buf45, primals_7, primals_8, buf48, 262144, XBLOCK=1024,
            num_warps=4, num_stages=1)
        del primals_8
        buf50 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf52 = reinterpret_tensor(buf50, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf50
        buf53 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_13[grid(256)](buf52,
            buf2, buf53, 256, 2304, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf54 = extern_kernels.convolution(buf48, buf53, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf55 = buf45
        del buf45
        buf56 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf58 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf54, buf55, buf56,
            buf58, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf59 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf54,
            buf55, buf56, primals_10, primals_11, buf59, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_11
        buf61 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf63 = reinterpret_tensor(buf61, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf61
        buf64 = empty_strided_cuda((1024, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_11[grid(1024)](buf63,
            primals_12, buf64, 1024, 256, num_warps=2, num_stages=1)
        buf65 = extern_kernels.convolution(buf59, buf64, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf65, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf66 = buf37
        del buf37
        triton_poi_fused_add_14[grid(1048576)](buf66, buf65, 1048576,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf67 = buf56
        del buf56
        buf68 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf70 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_15[grid(128)](buf66, buf67,
            buf68, buf70, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf71 = buf65
        del buf65
        triton_poi_fused_native_group_norm_relu_16[grid(1048576)](buf66,
            buf67, buf68, primals_13, primals_14, buf71, 1048576, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_14
        buf73 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf75 = reinterpret_tensor(buf73, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf73
        buf76 = empty_strided_cuda((256, 1024, 1, 1), (1024, 1, 1024, 1024),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_17[grid(256)](buf75,
            primals_15, buf76, 256, 1024, num_warps=8, num_stages=1)
        buf77 = extern_kernels.convolution(buf71, buf76, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf77, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf78 = buf68
        del buf68
        buf79 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf81 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf77, buf78, buf79,
            buf81, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf82 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf77,
            buf78, buf79, primals_16, primals_17, buf82, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_17
        buf84 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf86 = reinterpret_tensor(buf84, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf84
        buf87 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_13[grid(256)](buf86,
            buf3, buf87, 256, 2304, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf88 = extern_kernels.convolution(buf82, buf87, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf88, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf89 = buf79
        del buf79
        buf90 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf92 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf88, buf89, buf90,
            buf92, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf93 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf88,
            buf89, buf90, primals_19, primals_20, buf93, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_20
        buf95 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf97 = reinterpret_tensor(buf95, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf95
        buf98 = empty_strided_cuda((1024, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_11[grid(1024)](buf97,
            primals_21, buf98, 1024, 256, num_warps=2, num_stages=1)
        buf99 = extern_kernels.convolution(buf93, buf98, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf99, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf100 = buf99
        del buf99
        triton_poi_fused_add_18[grid(1048576)](buf100, buf66, 1048576,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf101 = buf90
        del buf90
        buf102 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf104 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_15[grid(128)](buf100, buf101,
            buf102, buf104, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf105 = reinterpret_tensor(buf23, (4, 1024, 16, 16), (262144, 1, 
            16384, 1024), 0)
        del buf23
        triton_poi_fused_native_group_norm_relu_16[grid(1048576)](buf100,
            buf101, buf102, primals_22, primals_23, buf105, 1048576, XBLOCK
            =1024, num_warps=4, num_stages=1)
        del primals_23
        buf107 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf109 = reinterpret_tensor(buf107, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf107
        buf110 = empty_strided_cuda((256, 1024, 1, 1), (1024, 1, 1024, 1024
            ), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_17[grid(256)](buf109,
            primals_24, buf110, 256, 1024, num_warps=8, num_stages=1)
        buf111 = extern_kernels.convolution(buf105, buf110, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf111, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf112 = buf102
        del buf102
        buf113 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf115 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf111, buf112,
            buf113, buf115, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf116 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf111,
            buf112, buf113, primals_25, primals_26, buf116, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_26
        buf118 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf120 = reinterpret_tensor(buf118, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf118
        buf121 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_13[grid(256)](buf120,
            buf4, buf121, 256, 2304, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf122 = extern_kernels.convolution(buf116, buf121, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf122, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf123 = buf113
        del buf113
        buf124 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf126 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf122, buf123,
            buf124, buf126, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf127 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf122,
            buf123, buf124, primals_28, primals_29, buf127, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_29
        buf129 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf131 = reinterpret_tensor(buf129, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf129
        buf132 = empty_strided_cuda((1024, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_11[grid(1024)](buf131,
            primals_30, buf132, 1024, 256, num_warps=2, num_stages=1)
        buf133 = extern_kernels.convolution(buf127, buf132, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf133, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf134 = buf133
        del buf133
        triton_poi_fused_add_18[grid(1048576)](buf134, buf100, 1048576,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf135 = buf124
        del buf124
        buf136 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf138 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_15[grid(128)](buf134, buf135,
            buf136, buf138, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf139 = empty_strided_cuda((4, 1024, 16, 16), (262144, 1, 16384, 
            1024), torch.float32)
        triton_poi_fused_native_group_norm_relu_16[grid(1048576)](buf134,
            buf135, buf136, primals_31, primals_32, buf139, 1048576, XBLOCK
            =1024, num_warps=4, num_stages=1)
        del primals_32
        buf141 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf143 = reinterpret_tensor(buf141, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf141
        buf144 = empty_strided_cuda((256, 1024, 1, 1), (1024, 1, 1024, 1024
            ), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_17[grid(256)](buf143,
            primals_33, buf144, 256, 1024, num_warps=8, num_stages=1)
        buf145 = extern_kernels.convolution(buf139, buf144, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf145, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf146 = buf136
        del buf136
        buf147 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf149 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf145, buf146,
            buf147, buf149, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf150 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf145,
            buf146, buf147, primals_34, primals_35, buf150, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_35
        buf152 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf154 = reinterpret_tensor(buf152, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf152
        buf155 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_13[grid(256)](buf154,
            buf5, buf155, 256, 2304, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf156 = extern_kernels.convolution(buf150, buf155, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf156, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf157 = buf147
        del buf147
        buf158 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf160 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf156, buf157,
            buf158, buf160, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf161 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf156,
            buf157, buf158, primals_37, primals_38, buf161, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_38
        buf163 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf165 = reinterpret_tensor(buf163, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf163
        buf166 = empty_strided_cuda((1024, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_11[grid(1024)](buf165,
            primals_39, buf166, 1024, 256, num_warps=2, num_stages=1)
        buf167 = extern_kernels.convolution(buf161, buf166, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf167, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf168 = buf167
        del buf167
        triton_poi_fused_add_18[grid(1048576)](buf168, buf134, 1048576,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf169 = buf158
        del buf158
        buf170 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf172 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_15[grid(128)](buf168, buf169,
            buf170, buf172, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf173 = empty_strided_cuda((4, 1024, 16, 16), (262144, 1, 16384, 
            1024), torch.float32)
        triton_poi_fused_native_group_norm_relu_16[grid(1048576)](buf168,
            buf169, buf170, primals_40, primals_41, buf173, 1048576, XBLOCK
            =1024, num_warps=4, num_stages=1)
        del primals_41
        buf175 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf177 = reinterpret_tensor(buf175, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf175
        buf178 = empty_strided_cuda((2048, 1024, 1, 1), (1024, 1, 1024, 
            1024), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_19[grid(2048)](buf177,
            primals_42, buf178, 2048, 1024, num_warps=8, num_stages=1)
        buf179 = extern_kernels.convolution(buf173, buf178, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf179, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf181 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf183 = reinterpret_tensor(buf181, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf181
        buf184 = empty_strided_cuda((512, 1024, 1, 1), (1024, 1, 1024, 1024
            ), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_20[grid(512)](buf183,
            primals_43, buf184, 512, 1024, num_warps=8, num_stages=1)
        buf185 = extern_kernels.convolution(buf173, buf184, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf185, (4, 512, 16, 16), (131072, 1, 8192, 512))
        buf186 = buf170
        del buf170
        buf187 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf189 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_21[grid(128)](buf185, buf186,
            buf187, buf189, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf190 = empty_strided_cuda((4, 512, 16, 16), (131072, 1, 8192, 512
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_22[grid(524288)](buf185,
            buf186, buf187, primals_44, primals_45, buf190, 524288, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_45
        buf192 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf194 = reinterpret_tensor(buf192, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf192
        buf195 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_23[grid(512)](buf194,
            buf6, buf195, 512, 4608, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf196 = extern_kernels.convolution(buf190, buf195, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf196, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf197 = buf187
        del buf187
        buf198 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf200 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf196, buf197,
            buf198, buf200, 128, 1024, num_warps=8, num_stages=1)
        buf201 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(131072)](buf196,
            buf197, buf198, primals_47, primals_48, buf201, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_48
        buf203 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf205 = reinterpret_tensor(buf203, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf203
        buf206 = empty_strided_cuda((2048, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_26[grid(2048)](buf205,
            primals_49, buf206, 2048, 512, num_warps=4, num_stages=1)
        buf207 = extern_kernels.convolution(buf201, buf206, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf207, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf208 = buf179
        del buf179
        triton_poi_fused_add_27[grid(524288)](buf208, buf207, 524288,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf209 = buf198
        del buf198
        buf210 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf212 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_28[grid(128)](buf208, buf209,
            buf210, buf212, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf213 = buf207
        del buf207
        triton_poi_fused_native_group_norm_relu_29[grid(524288)](buf208,
            buf209, buf210, primals_50, primals_51, buf213, 524288, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_51
        buf215 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf217 = reinterpret_tensor(buf215, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf215
        buf218 = empty_strided_cuda((512, 2048, 1, 1), (2048, 1, 2048, 2048
            ), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_30[grid(512)](buf217,
            primals_52, buf218, 512, 2048, XBLOCK=1, RBLOCK=2048, num_warps
            =16, num_stages=1)
        buf219 = extern_kernels.convolution(buf213, buf218, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf219, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf220 = buf210
        del buf210
        buf221 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf223 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf219, buf220,
            buf221, buf223, 128, 1024, num_warps=8, num_stages=1)
        buf224 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(131072)](buf219,
            buf220, buf221, primals_53, primals_54, buf224, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_54
        buf226 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf228 = reinterpret_tensor(buf226, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf226
        buf229 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_23[grid(512)](buf228,
            buf7, buf229, 512, 4608, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf230 = extern_kernels.convolution(buf224, buf229, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf230, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf231 = buf221
        del buf221
        buf232 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf234 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf230, buf231,
            buf232, buf234, 128, 1024, num_warps=8, num_stages=1)
        buf235 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(131072)](buf230,
            buf231, buf232, primals_56, primals_57, buf235, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_57
        buf237 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf239 = reinterpret_tensor(buf237, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf237
        buf240 = empty_strided_cuda((2048, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_26[grid(2048)](buf239,
            primals_58, buf240, 2048, 512, num_warps=4, num_stages=1)
        buf241 = extern_kernels.convolution(buf235, buf240, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf241, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf242 = buf241
        del buf241
        triton_poi_fused_add_31[grid(524288)](buf242, buf208, 524288,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf243 = buf232
        del buf232
        buf244 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf246 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_28[grid(128)](buf242, buf243,
            buf244, buf246, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf247 = empty_strided_cuda((4, 2048, 8, 8), (131072, 1, 16384, 
            2048), torch.float32)
        triton_poi_fused_native_group_norm_relu_29[grid(524288)](buf242,
            buf243, buf244, primals_59, primals_60, buf247, 524288, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_60
        buf249 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf251 = reinterpret_tensor(buf249, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf249
        buf252 = empty_strided_cuda((512, 2048, 1, 1), (2048, 1, 2048, 2048
            ), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_30[grid(512)](buf251,
            primals_61, buf252, 512, 2048, XBLOCK=1, RBLOCK=2048, num_warps
            =16, num_stages=1)
        buf253 = extern_kernels.convolution(buf247, buf252, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf253, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf254 = buf244
        del buf244
        buf255 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf257 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf253, buf254,
            buf255, buf257, 128, 1024, num_warps=8, num_stages=1)
        buf258 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(131072)](buf253,
            buf254, buf255, primals_62, primals_63, buf258, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_63
        buf260 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf262 = reinterpret_tensor(buf260, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf260
        buf263 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_23[grid(512)](buf262,
            buf8, buf263, 512, 4608, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf264 = extern_kernels.convolution(buf258, buf263, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf264, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf265 = buf255
        del buf255
        buf266 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf268 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf264, buf265,
            buf266, buf268, 128, 1024, num_warps=8, num_stages=1)
        buf269 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(131072)](buf264,
            buf265, buf266, primals_65, primals_66, buf269, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_66
        buf271 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf273 = reinterpret_tensor(buf271, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf271
        buf274 = empty_strided_cuda((2048, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_26[grid(2048)](buf273,
            primals_67, buf274, 2048, 512, num_warps=4, num_stages=1)
        buf275 = extern_kernels.convolution(buf269, buf274, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf275, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf276 = buf275
        del buf275
        triton_poi_fused_add_31[grid(524288)](buf276, buf242, 524288,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf277 = buf266
        del buf266
        buf278 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf280 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_28[grid(128)](buf276, buf277,
            buf278, buf280, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf281 = empty_strided_cuda((4, 2048, 8, 8), (131072, 1, 16384, 
            2048), torch.float32)
        triton_poi_fused_native_group_norm_relu_29[grid(524288)](buf276,
            buf277, buf278, primals_68, primals_69, buf281, 524288, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_69
        buf283 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf285 = reinterpret_tensor(buf283, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf283
        buf286 = empty_strided_cuda((512, 2048, 1, 1), (2048, 1, 2048, 2048
            ), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_30[grid(512)](buf285,
            primals_70, buf286, 512, 2048, XBLOCK=1, RBLOCK=2048, num_warps
            =16, num_stages=1)
        buf287 = extern_kernels.convolution(buf281, buf286, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf287, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf288 = buf278
        del buf278
        buf289 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf291 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf287, buf288,
            buf289, buf291, 128, 1024, num_warps=8, num_stages=1)
        buf292 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(131072)](buf287,
            buf288, buf289, primals_71, primals_72, buf292, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_72
        buf294 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf296 = reinterpret_tensor(buf294, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf294
        buf297 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_23[grid(512)](buf296,
            buf9, buf297, 512, 4608, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf298 = extern_kernels.convolution(buf292, buf297, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf298, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf299 = buf289
        del buf289
        buf300 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf302 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf298, buf299,
            buf300, buf302, 128, 1024, num_warps=8, num_stages=1)
        buf303 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(131072)](buf298,
            buf299, buf300, primals_74, primals_75, buf303, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_75
        buf305 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf307 = reinterpret_tensor(buf305, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf305
        buf308 = empty_strided_cuda((2048, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_26[grid(2048)](buf307,
            primals_76, buf308, 2048, 512, num_warps=4, num_stages=1)
        buf309 = extern_kernels.convolution(buf303, buf308, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf309, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf310 = buf309
        del buf309
        triton_poi_fused_add_31[grid(524288)](buf310, buf276, 524288,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf311 = buf300
        del buf300
        buf312 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf314 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_28[grid(128)](buf310, buf311,
            buf312, buf314, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf315 = empty_strided_cuda((4, 2048, 8, 8), (131072, 1, 16384, 
            2048), torch.float32)
        triton_poi_fused_native_group_norm_relu_29[grid(524288)](buf310,
            buf311, buf312, primals_77, primals_78, buf315, 524288, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_78
        buf317 = empty_strided_cuda((4096, 1, 1, 1), (1, 4096, 4096, 4096),
            torch.float32)
        buf319 = reinterpret_tensor(buf317, (4096, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf317
        buf320 = empty_strided_cuda((4096, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_32[grid(4096)](buf319,
            primals_79, buf320, 4096, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf321 = extern_kernels.convolution(buf315, buf320, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf321, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf323 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf325 = reinterpret_tensor(buf323, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf323
        buf326 = empty_strided_cuda((1024, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_33[grid(1024)](buf325,
            primals_80, buf326, 1024, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf327 = extern_kernels.convolution(buf315, buf326, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf327, (4, 1024, 8, 8), (65536, 1, 8192, 1024))
        buf328 = buf312
        del buf312
        buf329 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf331 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_34[grid(128)](buf327, buf328,
            buf329, buf331, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf332 = empty_strided_cuda((4, 1024, 8, 8), (65536, 1, 8192, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_35[grid(262144)](buf327,
            buf328, buf329, primals_81, primals_82, buf332, 262144, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_82
        buf334 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf336 = reinterpret_tensor(buf334, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf334
        buf337 = empty_strided_cuda((1024, 1024, 3, 3), (9216, 1, 3072, 
            1024), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_36[grid(1024)](buf336,
            buf10, buf337, 1024, 9216, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf338 = extern_kernels.convolution(buf332, buf337, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf338, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf339 = buf329
        del buf329
        buf340 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf342 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_37[grid(128)](buf338, buf339,
            buf340, buf342, 128, 512, num_warps=4, num_stages=1)
        buf343 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_38[grid(65536)](buf338,
            buf339, buf340, primals_84, primals_85, buf343, 65536, XBLOCK=
            512, num_warps=4, num_stages=1)
        del primals_85
        buf345 = empty_strided_cuda((4096, 1, 1, 1), (1, 4096, 4096, 4096),
            torch.float32)
        buf347 = reinterpret_tensor(buf345, (4096, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf345
        buf348 = empty_strided_cuda((4096, 1024, 1, 1), (1024, 1, 1024, 
            1024), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_39[grid(4096)](buf347,
            primals_86, buf348, 4096, 1024, num_warps=8, num_stages=1)
        buf349 = extern_kernels.convolution(buf343, buf348, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf349, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf350 = buf321
        del buf321
        triton_poi_fused_add_40[grid(262144)](buf350, buf349, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf351 = buf340
        del buf340
        buf352 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf354 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_41[grid(128)](buf350, buf351,
            buf352, buf354, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf355 = buf349
        del buf349
        triton_poi_fused_native_group_norm_relu_42[grid(262144)](buf350,
            buf351, buf352, primals_87, primals_88, buf355, 262144, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_88
        buf357 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf359 = reinterpret_tensor(buf357, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf357
        buf360 = empty_strided_cuda((1024, 4096, 1, 1), (4096, 1, 4096, 
            4096), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_43[grid(1024)](buf359,
            primals_89, buf360, 1024, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf361 = extern_kernels.convolution(buf355, buf360, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf361, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf362 = buf352
        del buf352
        buf363 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf365 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_37[grid(128)](buf361, buf362,
            buf363, buf365, 128, 512, num_warps=4, num_stages=1)
        buf366 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_38[grid(65536)](buf361,
            buf362, buf363, primals_90, primals_91, buf366, 65536, XBLOCK=
            512, num_warps=4, num_stages=1)
        del primals_91
        buf368 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf370 = reinterpret_tensor(buf368, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf368
        buf371 = empty_strided_cuda((1024, 1024, 3, 3), (9216, 1, 3072, 
            1024), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_36[grid(1024)](buf370,
            buf11, buf371, 1024, 9216, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf372 = extern_kernels.convolution(buf366, buf371, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf372, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf373 = buf363
        del buf363
        buf374 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf376 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_37[grid(128)](buf372, buf373,
            buf374, buf376, 128, 512, num_warps=4, num_stages=1)
        buf377 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_38[grid(65536)](buf372,
            buf373, buf374, primals_93, primals_94, buf377, 65536, XBLOCK=
            512, num_warps=4, num_stages=1)
        del primals_94
        buf379 = empty_strided_cuda((4096, 1, 1, 1), (1, 4096, 4096, 4096),
            torch.float32)
        buf381 = reinterpret_tensor(buf379, (4096, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf379
        buf382 = empty_strided_cuda((4096, 1024, 1, 1), (1024, 1, 1024, 
            1024), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_39[grid(4096)](buf381,
            primals_95, buf382, 4096, 1024, num_warps=8, num_stages=1)
        buf383 = extern_kernels.convolution(buf377, buf382, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf383, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf384 = buf383
        del buf383
        triton_poi_fused_add_44[grid(262144)](buf384, buf350, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf385 = buf374
        del buf374
        buf386 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf388 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_41[grid(128)](buf384, buf385,
            buf386, buf388, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf389 = empty_strided_cuda((4, 4096, 4, 4), (65536, 1, 16384, 4096
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_42[grid(262144)](buf384,
            buf385, buf386, primals_96, primals_97, buf389, 262144, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_97
        buf391 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf393 = reinterpret_tensor(buf391, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf391
        buf394 = empty_strided_cuda((1024, 4096, 1, 1), (4096, 1, 4096, 
            4096), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_43[grid(1024)](buf393,
            primals_98, buf394, 1024, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf395 = extern_kernels.convolution(buf389, buf394, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf395, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf396 = buf386
        del buf386
        buf397 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf399 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_37[grid(128)](buf395, buf396,
            buf397, buf399, 128, 512, num_warps=4, num_stages=1)
        buf400 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_38[grid(65536)](buf395,
            buf396, buf397, primals_99, primals_100, buf400, 65536, XBLOCK=
            512, num_warps=4, num_stages=1)
        del primals_100
        buf402 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf404 = reinterpret_tensor(buf402, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf402
        buf405 = empty_strided_cuda((1024, 1024, 3, 3), (9216, 1, 3072, 
            1024), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_36[grid(1024)](buf404,
            buf12, buf405, 1024, 9216, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf406 = extern_kernels.convolution(buf400, buf405, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf406, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf407 = buf397
        del buf397
        buf408 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf410 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_37[grid(128)](buf406, buf407,
            buf408, buf410, 128, 512, num_warps=4, num_stages=1)
        buf411 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_38[grid(65536)](buf406,
            buf407, buf408, primals_102, primals_103, buf411, 65536, XBLOCK
            =512, num_warps=4, num_stages=1)
        del primals_103
        buf413 = empty_strided_cuda((4096, 1, 1, 1), (1, 4096, 4096, 4096),
            torch.float32)
        buf415 = reinterpret_tensor(buf413, (4096, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf413
        buf416 = empty_strided_cuda((4096, 1024, 1, 1), (1024, 1, 1024, 
            1024), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_39[grid(4096)](buf415,
            primals_104, buf416, 4096, 1024, num_warps=8, num_stages=1)
        buf417 = extern_kernels.convolution(buf411, buf416, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf417, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf418 = buf417
        del buf417
        triton_poi_fused_add_44[grid(262144)](buf418, buf384, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf419 = buf408
        del buf408
        buf420 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf422 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_41[grid(128)](buf418, buf419,
            buf420, buf422, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf423 = empty_strided_cuda((4, 4096, 4, 4), (65536, 1, 16384, 4096
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_42[grid(262144)](buf418,
            buf419, buf420, primals_105, primals_106, buf423, 262144,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_106
        buf425 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf427 = reinterpret_tensor(buf425, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf425
        buf428 = empty_strided_cuda((1024, 4096, 1, 1), (4096, 1, 4096, 
            4096), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_43[grid(1024)](buf427,
            primals_107, buf428, 1024, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf429 = extern_kernels.convolution(buf423, buf428, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf429, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf430 = buf420
        del buf420
        buf431 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf433 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_37[grid(128)](buf429, buf430,
            buf431, buf433, 128, 512, num_warps=4, num_stages=1)
        buf434 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_38[grid(65536)](buf429,
            buf430, buf431, primals_108, primals_109, buf434, 65536, XBLOCK
            =512, num_warps=4, num_stages=1)
        del primals_109
        buf436 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf438 = reinterpret_tensor(buf436, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf436
        buf439 = empty_strided_cuda((1024, 1024, 3, 3), (9216, 1, 3072, 
            1024), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_36[grid(1024)](buf438,
            buf13, buf439, 1024, 9216, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf440 = extern_kernels.convolution(buf434, buf439, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf440, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf441 = buf431
        del buf431
        buf442 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf444 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_37[grid(128)](buf440, buf441,
            buf442, buf444, 128, 512, num_warps=4, num_stages=1)
        buf445 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_38[grid(65536)](buf440,
            buf441, buf442, primals_111, primals_112, buf445, 65536, XBLOCK
            =512, num_warps=4, num_stages=1)
        del primals_112
        buf447 = empty_strided_cuda((4096, 1, 1, 1), (1, 4096, 4096, 4096),
            torch.float32)
        buf449 = reinterpret_tensor(buf447, (4096, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf447
        buf450 = empty_strided_cuda((4096, 1024, 1, 1), (1024, 1, 1024, 
            1024), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_39[grid(4096)](buf449,
            primals_113, buf450, 4096, 1024, num_warps=8, num_stages=1)
        buf451 = extern_kernels.convolution(buf445, buf450, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf451, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf452 = buf451
        del buf451
        triton_poi_fused_add_44[grid(262144)](buf452, buf418, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf453 = buf442
        del buf442
        buf454 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf456 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_41[grid(128)](buf452, buf453,
            buf454, buf456, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf457 = empty_strided_cuda((4, 4096, 4, 4), (65536, 1, 16384, 4096
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_42[grid(262144)](buf452,
            buf453, buf454, primals_114, primals_115, buf457, 262144,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_115
        buf459 = empty_strided_cuda((8192, 1, 1, 1), (1, 8192, 8192, 8192),
            torch.float32)
        buf461 = reinterpret_tensor(buf459, (8192, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf459
        buf462 = empty_strided_cuda((8192, 4096, 1, 1), (4096, 1, 4096, 
            4096), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_45[grid(8192)](buf461,
            primals_116, buf462, 8192, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf463 = extern_kernels.convolution(buf457, buf462, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf463, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf465 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf467 = reinterpret_tensor(buf465, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf465
        buf468 = empty_strided_cuda((2048, 4096, 1, 1), (4096, 1, 4096, 
            4096), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_46[grid(2048)](buf467,
            primals_117, buf468, 2048, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf469 = extern_kernels.convolution(buf457, buf468, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf469, (4, 2048, 4, 4), (32768, 1, 8192, 2048))
        buf470 = buf454
        del buf454
        buf471 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf473 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_47[grid(128)](buf469, buf470,
            buf471, buf473, 128, 1024, num_warps=8, num_stages=1)
        buf474 = empty_strided_cuda((4, 2048, 4, 4), (32768, 1, 8192, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_48[grid(131072)](buf469,
            buf470, buf471, primals_118, primals_119, buf474, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_119
        buf476 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf478 = reinterpret_tensor(buf476, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf476
        buf479 = empty_strided_cuda((2048, 2048, 3, 3), (18432, 1, 6144, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_49[grid(2048)](buf478,
            buf14, buf479, 2048, 18432, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf480 = extern_kernels.convolution(buf474, buf479, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf480, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf481 = buf471
        del buf471
        buf482 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf484 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_50[grid(128)](buf480, buf481,
            buf482, buf484, 128, 256, num_warps=2, num_stages=1)
        buf485 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_51[grid(32768)](buf480,
            buf481, buf482, primals_121, primals_122, buf485, 32768, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_122
        buf487 = empty_strided_cuda((8192, 1, 1, 1), (1, 8192, 8192, 8192),
            torch.float32)
        buf489 = reinterpret_tensor(buf487, (8192, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf487
        buf490 = empty_strided_cuda((8192, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_52[grid(8192)](buf489,
            primals_123, buf490, 8192, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf491 = extern_kernels.convolution(buf485, buf490, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf491, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf492 = buf463
        del buf463
        triton_poi_fused_add_53[grid(131072)](buf492, buf491, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf493 = buf482
        del buf482
        buf494 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf496 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_54[grid(128)](buf492, buf493,
            buf494, buf496, 128, 1024, num_warps=8, num_stages=1)
        buf497 = buf491
        del buf491
        triton_poi_fused_native_group_norm_relu_55[grid(131072)](buf492,
            buf493, buf494, primals_124, primals_125, buf497, 131072,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_125
        buf499 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf501 = reinterpret_tensor(buf499, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf499
        buf502 = empty_strided_cuda((2048, 8192, 1, 1), (8192, 1, 8192, 
            8192), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_56[grid(2048)](buf501,
            primals_126, buf502, 2048, 8192, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf503 = extern_kernels.convolution(buf497, buf502, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf503, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf504 = buf494
        del buf494
        buf505 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf507 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_50[grid(128)](buf503, buf504,
            buf505, buf507, 128, 256, num_warps=2, num_stages=1)
        buf508 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_51[grid(32768)](buf503,
            buf504, buf505, primals_127, primals_128, buf508, 32768, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_128
        buf510 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf512 = reinterpret_tensor(buf510, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf510
        buf513 = empty_strided_cuda((2048, 2048, 3, 3), (18432, 1, 6144, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_49[grid(2048)](buf512,
            buf15, buf513, 2048, 18432, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf514 = extern_kernels.convolution(buf508, buf513, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf514, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf515 = buf505
        del buf505
        buf516 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf518 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_50[grid(128)](buf514, buf515,
            buf516, buf518, 128, 256, num_warps=2, num_stages=1)
        buf519 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_51[grid(32768)](buf514,
            buf515, buf516, primals_130, primals_131, buf519, 32768, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_131
        buf521 = empty_strided_cuda((8192, 1, 1, 1), (1, 8192, 8192, 8192),
            torch.float32)
        buf523 = reinterpret_tensor(buf521, (8192, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf521
        buf524 = empty_strided_cuda((8192, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_52[grid(8192)](buf523,
            primals_132, buf524, 8192, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf525 = extern_kernels.convolution(buf519, buf524, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf525, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf526 = buf525
        del buf525
        triton_poi_fused_add_57[grid(131072)](buf526, buf492, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf527 = buf516
        del buf516
        buf528 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf530 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_54[grid(128)](buf526, buf527,
            buf528, buf530, 128, 1024, num_warps=8, num_stages=1)
        buf531 = empty_strided_cuda((4, 8192, 2, 2), (32768, 1, 16384, 8192
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_55[grid(131072)](buf526,
            buf527, buf528, primals_133, primals_134, buf531, 131072,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_134
        buf533 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf535 = reinterpret_tensor(buf533, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf533
        buf536 = empty_strided_cuda((2048, 8192, 1, 1), (8192, 1, 8192, 
            8192), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_56[grid(2048)](buf535,
            primals_135, buf536, 2048, 8192, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf537 = extern_kernels.convolution(buf531, buf536, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf537, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf538 = buf528
        del buf528
        buf539 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf541 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_50[grid(128)](buf537, buf538,
            buf539, buf541, 128, 256, num_warps=2, num_stages=1)
        buf542 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_51[grid(32768)](buf537,
            buf538, buf539, primals_136, primals_137, buf542, 32768, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_137
        buf544 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf546 = reinterpret_tensor(buf544, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf544
        buf547 = empty_strided_cuda((2048, 2048, 3, 3), (18432, 1, 6144, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_49[grid(2048)](buf546,
            buf16, buf547, 2048, 18432, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf548 = extern_kernels.convolution(buf542, buf547, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf548, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf549 = buf539
        del buf539
        buf550 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf552 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_50[grid(128)](buf548, buf549,
            buf550, buf552, 128, 256, num_warps=2, num_stages=1)
        buf553 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_51[grid(32768)](buf548,
            buf549, buf550, primals_139, primals_140, buf553, 32768, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_140
        buf555 = empty_strided_cuda((8192, 1, 1, 1), (1, 8192, 8192, 8192),
            torch.float32)
        buf557 = reinterpret_tensor(buf555, (8192, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf555
        buf558 = empty_strided_cuda((8192, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_52[grid(8192)](buf557,
            primals_141, buf558, 8192, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf559 = extern_kernels.convolution(buf553, buf558, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf559, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf560 = buf559
        del buf559
        triton_poi_fused_add_57[grid(131072)](buf560, buf526, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf561 = buf550
        del buf550
        buf562 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf564 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_54[grid(128)](buf560, buf561,
            buf562, buf564, 128, 1024, num_warps=8, num_stages=1)
        buf565 = empty_strided_cuda((4, 8192, 2, 2), (32768, 1, 16384, 8192
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_55[grid(131072)](buf560,
            buf561, buf562, primals_142, primals_143, buf565, 131072,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_143
        buf567 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf569 = reinterpret_tensor(buf567, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf567
        buf570 = empty_strided_cuda((2048, 8192, 1, 1), (8192, 1, 8192, 
            8192), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_56[grid(2048)](buf569,
            primals_144, buf570, 2048, 8192, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf571 = extern_kernels.convolution(buf565, buf570, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf571, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf572 = buf562
        del buf562
        buf573 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf575 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_50[grid(128)](buf571, buf572,
            buf573, buf575, 128, 256, num_warps=2, num_stages=1)
        buf576 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_51[grid(32768)](buf571,
            buf572, buf573, primals_145, primals_146, buf576, 32768, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_146
        buf578 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf580 = reinterpret_tensor(buf578, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf578
        buf581 = empty_strided_cuda((2048, 2048, 3, 3), (18432, 1, 6144, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_49[grid(2048)](buf580,
            buf17, buf581, 2048, 18432, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf582 = extern_kernels.convolution(buf576, buf581, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf582, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf583 = buf573
        del buf573
        buf584 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf586 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_50[grid(128)](buf582, buf583,
            buf584, buf586, 128, 256, num_warps=2, num_stages=1)
        buf587 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_51[grid(32768)](buf582,
            buf583, buf584, primals_148, primals_149, buf587, 32768, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_149
        buf589 = empty_strided_cuda((8192, 1, 1, 1), (1, 8192, 8192, 8192),
            torch.float32)
        buf591 = reinterpret_tensor(buf589, (8192, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf589
        buf592 = empty_strided_cuda((8192, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_52[grid(8192)](buf591,
            primals_150, buf592, 8192, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf593 = extern_kernels.convolution(buf587, buf592, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf593, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf594 = buf593
        del buf593
        triton_poi_fused_add_57[grid(131072)](buf594, buf560, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf595 = reinterpret_tensor(buf584, (4, 32, 1, 1), (32, 1, 32, 32), 0)
        del buf584
        buf596 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf598 = reinterpret_tensor(buf596, (4, 32, 1, 1), (32, 1, 32, 32), 0)
        del buf596
        triton_per_fused_native_group_norm_58[grid(128)](buf598, buf594,
            buf595, 128, 1024, num_warps=8, num_stages=1)
        buf599 = empty_strided_cuda((4, 8192, 1, 1), (8192, 1, 8192, 8192),
            torch.float32)
        triton_poi_fused_mean_native_group_norm_relu_59[grid(32768)](buf594,
            buf595, buf598, primals_151, primals_152, buf599, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf600 = extern_kernels.convolution(buf599, primals_153, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf600, (4, 21843, 1, 1), (21843, 1, 21843, 21843))
        buf601 = reinterpret_tensor(buf600, (4, 21843, 1, 1), (21843, 1, 
            87372, 87372), 0)
        del buf600
        triton_poi_fused_convolution_60[grid(87372)](buf601, primals_154, 
            87372, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_154
    return (reinterpret_tensor(buf601, (4, 21843), (21843, 1), 0), buf0,
        buf1, primals_3, primals_5, primals_6, primals_7, buf2, primals_10,
        primals_12, primals_13, primals_15, primals_16, buf3, primals_19,
        primals_21, primals_22, primals_24, primals_25, buf4, primals_28,
        primals_30, primals_31, primals_33, primals_34, buf5, primals_37,
        primals_39, primals_40, primals_42, primals_43, primals_44, buf6,
        primals_47, primals_49, primals_50, primals_52, primals_53, buf7,
        primals_56, primals_58, primals_59, primals_61, primals_62, buf8,
        primals_65, primals_67, primals_68, primals_70, primals_71, buf9,
        primals_74, primals_76, primals_77, primals_79, primals_80,
        primals_81, buf10, primals_84, primals_86, primals_87, primals_89,
        primals_90, buf11, primals_93, primals_95, primals_96, primals_98,
        primals_99, buf12, primals_102, primals_104, primals_105,
        primals_107, primals_108, buf13, primals_111, primals_113,
        primals_114, primals_116, primals_117, primals_118, buf14,
        primals_121, primals_123, primals_124, primals_126, primals_127,
        buf15, primals_130, primals_132, primals_133, primals_135,
        primals_136, buf16, primals_139, primals_141, primals_142,
        primals_144, primals_145, buf17, primals_148, primals_150,
        primals_151, primals_152, primals_153, buf21, buf22, buf24, buf25,
        buf26, reinterpret_tensor(buf27, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf30, (4, 32), (32, 1), 0), buf31, buf35, buf36,
        buf41, buf42, buf43, reinterpret_tensor(buf44, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf47, (4, 32), (32, 1), 0), buf48, buf52, buf53,
        buf54, reinterpret_tensor(buf55, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf58, (4, 32), (32, 1), 0), buf59, buf63, buf64,
        buf66, reinterpret_tensor(buf67, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf70, (4, 32), (32, 1), 0), buf71, buf75, buf76,
        buf77, reinterpret_tensor(buf78, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf81, (4, 32), (32, 1), 0), buf82, buf86, buf87,
        buf88, reinterpret_tensor(buf89, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf92, (4, 32), (32, 1), 0), buf93, buf97, buf98,
        buf100, reinterpret_tensor(buf101, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf104, (4, 32), (32, 1), 0), buf105, buf109,
        buf110, buf111, reinterpret_tensor(buf112, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf115, (4, 32), (32, 1), 0), buf116, buf120,
        buf121, buf122, reinterpret_tensor(buf123, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf126, (4, 32), (32, 1), 0), buf127, buf131,
        buf132, buf134, reinterpret_tensor(buf135, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf138, (4, 32), (32, 1), 0), buf139, buf143,
        buf144, buf145, reinterpret_tensor(buf146, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf149, (4, 32), (32, 1), 0), buf150, buf154,
        buf155, buf156, reinterpret_tensor(buf157, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf160, (4, 32), (32, 1), 0), buf161, buf165,
        buf166, buf168, reinterpret_tensor(buf169, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf172, (4, 32), (32, 1), 0), buf173, buf177,
        buf178, buf183, buf184, buf185, reinterpret_tensor(buf186, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf189, (4, 32), (32, 1), 0),
        buf190, buf194, buf195, buf196, reinterpret_tensor(buf197, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf200, (4, 32), (32, 1), 0),
        buf201, buf205, buf206, buf208, reinterpret_tensor(buf209, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf212, (4, 32), (32, 1), 0),
        buf213, buf217, buf218, buf219, reinterpret_tensor(buf220, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf223, (4, 32), (32, 1), 0),
        buf224, buf228, buf229, buf230, reinterpret_tensor(buf231, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf234, (4, 32), (32, 1), 0),
        buf235, buf239, buf240, buf242, reinterpret_tensor(buf243, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf246, (4, 32), (32, 1), 0),
        buf247, buf251, buf252, buf253, reinterpret_tensor(buf254, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf257, (4, 32), (32, 1), 0),
        buf258, buf262, buf263, buf264, reinterpret_tensor(buf265, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf268, (4, 32), (32, 1), 0),
        buf269, buf273, buf274, buf276, reinterpret_tensor(buf277, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf280, (4, 32), (32, 1), 0),
        buf281, buf285, buf286, buf287, reinterpret_tensor(buf288, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf291, (4, 32), (32, 1), 0),
        buf292, buf296, buf297, buf298, reinterpret_tensor(buf299, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf302, (4, 32), (32, 1), 0),
        buf303, buf307, buf308, buf310, reinterpret_tensor(buf311, (4, 32),
        (32, 1), 0), reinterpret_tensor(buf314, (4, 32), (32, 1), 0),
        buf315, buf319, buf320, buf325, buf326, buf327, reinterpret_tensor(
        buf328, (4, 32), (32, 1), 0), reinterpret_tensor(buf331, (4, 32), (
        32, 1), 0), buf332, buf336, buf337, buf338, reinterpret_tensor(
        buf339, (4, 32), (32, 1), 0), reinterpret_tensor(buf342, (4, 32), (
        32, 1), 0), buf343, buf347, buf348, buf350, reinterpret_tensor(
        buf351, (4, 32), (32, 1), 0), reinterpret_tensor(buf354, (4, 32), (
        32, 1), 0), buf355, buf359, buf360, buf361, reinterpret_tensor(
        buf362, (4, 32), (32, 1), 0), reinterpret_tensor(buf365, (4, 32), (
        32, 1), 0), buf366, buf370, buf371, buf372, reinterpret_tensor(
        buf373, (4, 32), (32, 1), 0), reinterpret_tensor(buf376, (4, 32), (
        32, 1), 0), buf377, buf381, buf382, buf384, reinterpret_tensor(
        buf385, (4, 32), (32, 1), 0), reinterpret_tensor(buf388, (4, 32), (
        32, 1), 0), buf389, buf393, buf394, buf395, reinterpret_tensor(
        buf396, (4, 32), (32, 1), 0), reinterpret_tensor(buf399, (4, 32), (
        32, 1), 0), buf400, buf404, buf405, buf406, reinterpret_tensor(
        buf407, (4, 32), (32, 1), 0), reinterpret_tensor(buf410, (4, 32), (
        32, 1), 0), buf411, buf415, buf416, buf418, reinterpret_tensor(
        buf419, (4, 32), (32, 1), 0), reinterpret_tensor(buf422, (4, 32), (
        32, 1), 0), buf423, buf427, buf428, buf429, reinterpret_tensor(
        buf430, (4, 32), (32, 1), 0), reinterpret_tensor(buf433, (4, 32), (
        32, 1), 0), buf434, buf438, buf439, buf440, reinterpret_tensor(
        buf441, (4, 32), (32, 1), 0), reinterpret_tensor(buf444, (4, 32), (
        32, 1), 0), buf445, buf449, buf450, buf452, reinterpret_tensor(
        buf453, (4, 32), (32, 1), 0), reinterpret_tensor(buf456, (4, 32), (
        32, 1), 0), buf457, buf461, buf462, buf467, buf468, buf469,
        reinterpret_tensor(buf470, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf473, (4, 32), (32, 1), 0), buf474, buf478, buf479, buf480,
        reinterpret_tensor(buf481, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf484, (4, 32), (32, 1), 0), buf485, buf489, buf490, buf492,
        reinterpret_tensor(buf493, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf496, (4, 32), (32, 1), 0), buf497, buf501, buf502, buf503,
        reinterpret_tensor(buf504, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf507, (4, 32), (32, 1), 0), buf508, buf512, buf513, buf514,
        reinterpret_tensor(buf515, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf518, (4, 32), (32, 1), 0), buf519, buf523, buf524, buf526,
        reinterpret_tensor(buf527, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf530, (4, 32), (32, 1), 0), buf531, buf535, buf536, buf537,
        reinterpret_tensor(buf538, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf541, (4, 32), (32, 1), 0), buf542, buf546, buf547, buf548,
        reinterpret_tensor(buf549, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf552, (4, 32), (32, 1), 0), buf553, buf557, buf558, buf560,
        reinterpret_tensor(buf561, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf564, (4, 32), (32, 1), 0), buf565, buf569, buf570, buf571,
        reinterpret_tensor(buf572, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf575, (4, 32), (32, 1), 0), buf576, buf580, buf581, buf582,
        reinterpret_tensor(buf583, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf586, (4, 32), (32, 1), 0), buf587, buf591, buf592, buf594,
        buf595, buf598, buf599)


def conv1x1(cin, cout, stride=1, bias=False):
    return StdConv2d(cin, cout, kernel_size=1, stride=stride, padding=0,
        bias=bias)


def conv3x3(cin, cout, stride=1, groups=1, bias=False):
    return StdConv2d(cin, cout, kernel_size=3, stride=stride, padding=1,
        bias=bias, groups=groups)


def tf2th(conv_weights):
    """Possibly convert HWIO to OIHW"""
    if conv_weights.ndim == 4:
        conv_weights = np.transpose(conv_weights, [3, 2, 0, 1])
    return torch.from_numpy(conv_weights)


class StdConv2d(nn.Conv2d):

    def forward(self, x):
        w = self.weight
        v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
        w = (w - m) / torch.sqrt(v + 1e-10)
        return F.conv2d(x, w, self.bias, self.stride, self.padding, self.
            dilation, self.groups)


class PreActBottleneck(nn.Module):
    """
  Follows the implementation of "Identity Mappings in Deep Residual Networks" here:
  https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua

  Except it puts the stride on 3x3 conv when available.
  """

    def __init__(self, cin, cout=None, cmid=None, stride=1):
        super().__init__()
        cout = cout or cin
        cmid = cmid or cout // 4
        self.gn1 = nn.GroupNorm(32, cin)
        self.conv1 = conv1x1(cin, cmid)
        self.gn2 = nn.GroupNorm(32, cmid)
        self.conv2 = conv3x3(cmid, cmid, stride)
        self.gn3 = nn.GroupNorm(32, cmid)
        self.conv3 = conv1x1(cmid, cout)
        self.relu = nn.ReLU(inplace=True)
        if stride != 1 or cin != cout:
            self.downsample = conv1x1(cin, cout, stride)

    def forward(self, x):
        out = self.relu(self.gn1(x))
        residual = x
        if hasattr(self, 'downsample'):
            residual = self.downsample(out)
        out = self.conv1(out)
        out = self.conv2(self.relu(self.gn2(out)))
        out = self.conv3(self.relu(self.gn3(out)))
        return out + residual

    def load_from(self, weights, prefix=''):
        with torch.no_grad():
            self.conv1.weight.copy_(tf2th(weights[prefix +
                'a/standardized_conv2d/kernel']))
            self.conv2.weight.copy_(tf2th(weights[prefix +
                'b/standardized_conv2d/kernel']))
            self.conv3.weight.copy_(tf2th(weights[prefix +
                'c/standardized_conv2d/kernel']))
            self.gn1.weight.copy_(tf2th(weights[prefix + 'a/group_norm/gamma'])
                )
            self.gn2.weight.copy_(tf2th(weights[prefix + 'b/group_norm/gamma'])
                )
            self.gn3.weight.copy_(tf2th(weights[prefix + 'c/group_norm/gamma'])
                )
            self.gn1.bias.copy_(tf2th(weights[prefix + 'a/group_norm/beta']))
            self.gn2.bias.copy_(tf2th(weights[prefix + 'b/group_norm/beta']))
            self.gn3.bias.copy_(tf2th(weights[prefix + 'c/group_norm/beta']))
            if hasattr(self, 'downsample'):
                self.downsample.weight.copy_(tf2th(weights[prefix +
                    'a/proj/standardized_conv2d/kernel']))
        return self


class ResNetV2New(nn.Module):
    BLOCK_UNITS = {'r50': [3, 4, 6, 3], 'r101': [3, 4, 23, 3], 'r152': [3, 
        8, 36, 3]}

    def __init__(self, block_units, width_factor, head_size=21843,
        zero_head=False):
        super().__init__()
        wf = width_factor
        self.root = nn.Sequential(OrderedDict([('conv', StdConv2d(3, 64 *
            wf, kernel_size=7, stride=2, padding=3, bias=False)), ('padp',
            nn.ConstantPad2d(1, 0)), ('pool', nn.MaxPool2d(kernel_size=3,
            stride=2, padding=0))]))
        self.body = nn.Sequential(OrderedDict([('block1', nn.Sequential(
            OrderedDict([('unit01', PreActBottleneck(cin=64 * wf, cout=256 *
            wf, cmid=64 * wf))] + [(f'unit{i:02d}', PreActBottleneck(cin=
            256 * wf, cout=256 * wf, cmid=64 * wf)) for i in range(2, 
            block_units[0] + 1)]))), ('block2', nn.Sequential(OrderedDict([
            ('unit01', PreActBottleneck(cin=256 * wf, cout=512 * wf, cmid=
            128 * wf, stride=2))] + [(f'unit{i:02d}', PreActBottleneck(cin=
            512 * wf, cout=512 * wf, cmid=128 * wf)) for i in range(2, 
            block_units[1] + 1)]))), ('block3', nn.Sequential(OrderedDict([
            ('unit01', PreActBottleneck(cin=512 * wf, cout=1024 * wf, cmid=
            256 * wf, stride=2))] + [(f'unit{i:02d}', PreActBottleneck(cin=
            1024 * wf, cout=1024 * wf, cmid=256 * wf)) for i in range(2, 
            block_units[2] + 1)]))), ('block4', nn.Sequential(OrderedDict([
            ('unit01', PreActBottleneck(cin=1024 * wf, cout=2048 * wf, cmid
            =512 * wf, stride=2))] + [(f'unit{i:02d}', PreActBottleneck(cin
            =2048 * wf, cout=2048 * wf, cmid=512 * wf)) for i in range(2, 
            block_units[3] + 1)])))]))
        self.zero_head = zero_head
        self.head = nn.Sequential(OrderedDict([('gn', nn.GroupNorm(32, 2048 *
            wf)), ('relu', nn.ReLU(inplace=True)), ('avg', nn.
            AdaptiveAvgPool2d(output_size=1)), ('conv', nn.Conv2d(2048 * wf,
            head_size, kernel_size=1, bias=True))]))

    def load_from(self, weights, prefix='resnet/'):
        with torch.no_grad():
            self.root.conv.weight.copy_(tf2th(weights[
                f'{prefix}root_block/standardized_conv2d/kernel']))
            self.head.gn.weight.copy_(tf2th(weights[
                f'{prefix}group_norm/gamma']))
            self.head.gn.bias.copy_(tf2th(weights[f'{prefix}group_norm/beta']))
            if self.zero_head:
                nn.init.zeros_(self.head.conv.weight)
                nn.init.zeros_(self.head.conv.bias)
            else:
                self.head.conv.weight.copy_(tf2th(weights[
                    f'{prefix}head/conv2d/kernel']))
                self.head.conv.bias.copy_(tf2th(weights[
                    f'{prefix}head/conv2d/bias']))
            for bname, block in self.body.named_children():
                for uname, unit in block.named_children():
                    unit.load_from(weights, prefix=f'{prefix}{bname}/{uname}/')
        return self

    def forward(self, input_0):
        primals_1 = self.root.conv.weight
        primals_3 = self.body.block1.unit01.gn1.weight
        primals_4 = self.body.block1.unit01.gn1.bias
        primals_6 = self.body.block1.unit01.conv1.weight
        primals_7 = self.body.block1.unit01.gn2.weight
        primals_8 = self.body.block1.unit01.gn2.bias
        primals_9 = self.body.block1.unit01.conv2.weight
        primals_10 = self.body.block1.unit01.gn3.weight
        primals_11 = self.body.block1.unit01.gn3.bias
        primals_5 = self.body.block1.unit01.conv3.weight
        primals_12 = self.body.block1.unit01.downsample.weight
        primals_13 = self.body.block1.unit02.gn1.weight
        primals_14 = self.body.block1.unit02.gn1.bias
        primals_15 = self.body.block1.unit02.conv1.weight
        primals_16 = self.body.block1.unit02.gn2.weight
        primals_17 = self.body.block1.unit02.gn2.bias
        primals_18 = self.body.block1.unit02.conv2.weight
        primals_19 = self.body.block1.unit02.gn3.weight
        primals_20 = self.body.block1.unit02.gn3.bias
        primals_21 = self.body.block1.unit02.conv3.weight
        primals_22 = self.body.block1.unit03.gn1.weight
        primals_23 = self.body.block1.unit03.gn1.bias
        primals_24 = self.body.block1.unit03.conv1.weight
        primals_25 = self.body.block1.unit03.gn2.weight
        primals_26 = self.body.block1.unit03.gn2.bias
        primals_27 = self.body.block1.unit03.conv2.weight
        primals_28 = self.body.block1.unit03.gn3.weight
        primals_29 = self.body.block1.unit03.gn3.bias
        primals_30 = self.body.block1.unit03.conv3.weight
        primals_31 = self.body.block1.unit04.gn1.weight
        primals_32 = self.body.block1.unit04.gn1.bias
        primals_33 = self.body.block1.unit04.conv1.weight
        primals_34 = self.body.block1.unit04.gn2.weight
        primals_35 = self.body.block1.unit04.gn2.bias
        primals_36 = self.body.block1.unit04.conv2.weight
        primals_37 = self.body.block1.unit04.gn3.weight
        primals_38 = self.body.block1.unit04.gn3.bias
        primals_39 = self.body.block1.unit04.conv3.weight
        primals_40 = self.body.block2.unit01.gn1.weight
        primals_41 = self.body.block2.unit01.gn1.bias
        primals_43 = self.body.block2.unit01.conv1.weight
        primals_44 = self.body.block2.unit01.gn2.weight
        primals_45 = self.body.block2.unit01.gn2.bias
        primals_46 = self.body.block2.unit01.conv2.weight
        primals_47 = self.body.block2.unit01.gn3.weight
        primals_48 = self.body.block2.unit01.gn3.bias
        primals_49 = self.body.block2.unit01.conv3.weight
        primals_42 = self.body.block2.unit01.downsample.weight
        primals_50 = self.body.block2.unit02.gn1.weight
        primals_51 = self.body.block2.unit02.gn1.bias
        primals_52 = self.body.block2.unit02.conv1.weight
        primals_53 = self.body.block2.unit02.gn2.weight
        primals_54 = self.body.block2.unit02.gn2.bias
        primals_55 = self.body.block2.unit02.conv2.weight
        primals_56 = self.body.block2.unit02.gn3.weight
        primals_57 = self.body.block2.unit02.gn3.bias
        primals_58 = self.body.block2.unit02.conv3.weight
        primals_59 = self.body.block2.unit03.gn1.weight
        primals_60 = self.body.block2.unit03.gn1.bias
        primals_61 = self.body.block2.unit03.conv1.weight
        primals_62 = self.body.block2.unit03.gn2.weight
        primals_63 = self.body.block2.unit03.gn2.bias
        primals_64 = self.body.block2.unit03.conv2.weight
        primals_65 = self.body.block2.unit03.gn3.weight
        primals_66 = self.body.block2.unit03.gn3.bias
        primals_67 = self.body.block2.unit03.conv3.weight
        primals_68 = self.body.block2.unit04.gn1.weight
        primals_69 = self.body.block2.unit04.gn1.bias
        primals_70 = self.body.block2.unit04.conv1.weight
        primals_71 = self.body.block2.unit04.gn2.weight
        primals_72 = self.body.block2.unit04.gn2.bias
        primals_73 = self.body.block2.unit04.conv2.weight
        primals_74 = self.body.block2.unit04.gn3.weight
        primals_75 = self.body.block2.unit04.gn3.bias
        primals_76 = self.body.block2.unit04.conv3.weight
        primals_77 = self.body.block3.unit01.gn1.weight
        primals_78 = self.body.block3.unit01.gn1.bias
        primals_80 = self.body.block3.unit01.conv1.weight
        primals_81 = self.body.block3.unit01.gn2.weight
        primals_82 = self.body.block3.unit01.gn2.bias
        primals_83 = self.body.block3.unit01.conv2.weight
        primals_84 = self.body.block3.unit01.gn3.weight
        primals_85 = self.body.block3.unit01.gn3.bias
        primals_86 = self.body.block3.unit01.conv3.weight
        primals_79 = self.body.block3.unit01.downsample.weight
        primals_87 = self.body.block3.unit02.gn1.weight
        primals_88 = self.body.block3.unit02.gn1.bias
        primals_89 = self.body.block3.unit02.conv1.weight
        primals_90 = self.body.block3.unit02.gn2.weight
        primals_91 = self.body.block3.unit02.gn2.bias
        primals_92 = self.body.block3.unit02.conv2.weight
        primals_93 = self.body.block3.unit02.gn3.weight
        primals_94 = self.body.block3.unit02.gn3.bias
        primals_95 = self.body.block3.unit02.conv3.weight
        primals_96 = self.body.block3.unit03.gn1.weight
        primals_97 = self.body.block3.unit03.gn1.bias
        primals_98 = self.body.block3.unit03.conv1.weight
        primals_99 = self.body.block3.unit03.gn2.weight
        primals_100 = self.body.block3.unit03.gn2.bias
        primals_101 = self.body.block3.unit03.conv2.weight
        primals_102 = self.body.block3.unit03.gn3.weight
        primals_103 = self.body.block3.unit03.gn3.bias
        primals_104 = self.body.block3.unit03.conv3.weight
        primals_105 = self.body.block3.unit04.gn1.weight
        primals_106 = self.body.block3.unit04.gn1.bias
        primals_107 = self.body.block3.unit04.conv1.weight
        primals_108 = self.body.block3.unit04.gn2.weight
        primals_109 = self.body.block3.unit04.gn2.bias
        primals_110 = self.body.block3.unit04.conv2.weight
        primals_111 = self.body.block3.unit04.gn3.weight
        primals_112 = self.body.block3.unit04.gn3.bias
        primals_113 = self.body.block3.unit04.conv3.weight
        primals_114 = self.body.block4.unit01.gn1.weight
        primals_115 = self.body.block4.unit01.gn1.bias
        primals_117 = self.body.block4.unit01.conv1.weight
        primals_118 = self.body.block4.unit01.gn2.weight
        primals_119 = self.body.block4.unit01.gn2.bias
        primals_120 = self.body.block4.unit01.conv2.weight
        primals_121 = self.body.block4.unit01.gn3.weight
        primals_122 = self.body.block4.unit01.gn3.bias
        primals_123 = self.body.block4.unit01.conv3.weight
        primals_116 = self.body.block4.unit01.downsample.weight
        primals_124 = self.body.block4.unit02.gn1.weight
        primals_125 = self.body.block4.unit02.gn1.bias
        primals_126 = self.body.block4.unit02.conv1.weight
        primals_127 = self.body.block4.unit02.gn2.weight
        primals_128 = self.body.block4.unit02.gn2.bias
        primals_129 = self.body.block4.unit02.conv2.weight
        primals_130 = self.body.block4.unit02.gn3.weight
        primals_131 = self.body.block4.unit02.gn3.bias
        primals_132 = self.body.block4.unit02.conv3.weight
        primals_133 = self.body.block4.unit03.gn1.weight
        primals_134 = self.body.block4.unit03.gn1.bias
        primals_135 = self.body.block4.unit03.conv1.weight
        primals_136 = self.body.block4.unit03.gn2.weight
        primals_137 = self.body.block4.unit03.gn2.bias
        primals_138 = self.body.block4.unit03.conv2.weight
        primals_139 = self.body.block4.unit03.gn3.weight
        primals_140 = self.body.block4.unit03.gn3.bias
        primals_141 = self.body.block4.unit03.conv3.weight
        primals_142 = self.body.block4.unit04.gn1.weight
        primals_143 = self.body.block4.unit04.gn1.bias
        primals_144 = self.body.block4.unit04.conv1.weight
        primals_145 = self.body.block4.unit04.gn2.weight
        primals_146 = self.body.block4.unit04.gn2.bias
        primals_147 = self.body.block4.unit04.conv2.weight
        primals_148 = self.body.block4.unit04.gn3.weight
        primals_149 = self.body.block4.unit04.gn3.bias
        primals_150 = self.body.block4.unit04.conv3.weight
        primals_151 = self.head.gn.weight
        primals_152 = self.head.gn.bias
        primals_153 = self.head.conv.weight
        primals_154 = self.head.conv.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38, primals_39,
            primals_40, primals_41, primals_42, primals_43, primals_44,
            primals_45, primals_46, primals_47, primals_48, primals_49,
            primals_50, primals_51, primals_52, primals_53, primals_54,
            primals_55, primals_56, primals_57, primals_58, primals_59,
            primals_60, primals_61, primals_62, primals_63, primals_64,
            primals_65, primals_66, primals_67, primals_68, primals_69,
            primals_70, primals_71, primals_72, primals_73, primals_74,
            primals_75, primals_76, primals_77, primals_78, primals_79,
            primals_80, primals_81, primals_82, primals_83, primals_84,
            primals_85, primals_86, primals_87, primals_88, primals_89,
            primals_90, primals_91, primals_92, primals_93, primals_94,
            primals_95, primals_96, primals_97, primals_98, primals_99,
            primals_100, primals_101, primals_102, primals_103, primals_104,
            primals_105, primals_106, primals_107, primals_108, primals_109,
            primals_110, primals_111, primals_112, primals_113, primals_114,
            primals_115, primals_116, primals_117, primals_118, primals_119,
            primals_120, primals_121, primals_122, primals_123, primals_124,
            primals_125, primals_126, primals_127, primals_128, primals_129,
            primals_130, primals_131, primals_132, primals_133, primals_134,
            primals_135, primals_136, primals_137, primals_138, primals_139,
            primals_140, primals_141, primals_142, primals_143, primals_144,
            primals_145, primals_146, primals_147, primals_148, primals_149,
            primals_150, primals_151, primals_152, primals_153, primals_154])
        return output[0]


Measured latency_ms: 0.037053