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 torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from collections import OrderedDict
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_poi_fused_add_13(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_14(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_15(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_16(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_17(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_18(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_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_red_fused_native_group_norm_20(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_21(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_per_fused_native_group_norm_22(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_23(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_24(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_25(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_26(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_27(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_28(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_29(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_30(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_31(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_32(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_33(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_per_fused_native_group_norm_34(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_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 // 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_36(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_37(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_38(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_39(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_40(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_41(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_42(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_43(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_44(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_45(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_per_fused_native_group_norm_46(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_47(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_48(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_49(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_50(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_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 % 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_52(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_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_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_54(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_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)
    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_56(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
        buf49 = extern_kernels.convolution(buf48, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf49, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf50 = buf45
        del buf45
        buf51 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf53 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf49, buf50, buf51,
            buf53, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf54 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf49,
            buf50, buf51, primals_10, primals_11, buf54, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_11
        buf56 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf58 = reinterpret_tensor(buf56, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf56
        buf59 = 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)](buf58,
            primals_12, buf59, 1024, 256, num_warps=2, num_stages=1)
        buf60 = extern_kernels.convolution(buf54, buf59, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf60, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf61 = buf37
        del buf37
        triton_poi_fused_add_13[grid(1048576)](buf61, buf60, 1048576,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf62 = buf51
        del buf51
        buf63 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf65 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_14[grid(128)](buf61, buf62,
            buf63, buf65, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf66 = buf60
        del buf60
        triton_poi_fused_native_group_norm_relu_15[grid(1048576)](buf61,
            buf62, buf63, primals_13, primals_14, buf66, 1048576, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_14
        buf68 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf70 = reinterpret_tensor(buf68, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf68
        buf71 = empty_strided_cuda((256, 1024, 1, 1), (1024, 1, 1024, 1024),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_16[grid(256)](buf70,
            primals_15, buf71, 256, 1024, num_warps=8, num_stages=1)
        buf72 = extern_kernels.convolution(buf66, buf71, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf72, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf73 = buf63
        del buf63
        buf74 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf76 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf72, buf73, buf74,
            buf76, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf77 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf72,
            buf73, buf74, primals_16, primals_17, buf77, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_17
        buf78 = extern_kernels.convolution(buf77, buf3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf78, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf79 = buf74
        del buf74
        buf80 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf82 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf78, buf79, buf80,
            buf82, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf83 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf78,
            buf79, buf80, primals_19, primals_20, buf83, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_20
        buf85 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf87 = reinterpret_tensor(buf85, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf85
        buf88 = 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)](buf87,
            primals_21, buf88, 1024, 256, num_warps=2, num_stages=1)
        buf89 = extern_kernels.convolution(buf83, buf88, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf89, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf90 = buf89
        del buf89
        triton_poi_fused_add_17[grid(1048576)](buf90, buf61, 1048576,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf91 = buf80
        del buf80
        buf92 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf94 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_14[grid(128)](buf90, buf91,
            buf92, buf94, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf95 = reinterpret_tensor(buf23, (4, 1024, 16, 16), (262144, 1, 
            16384, 1024), 0)
        del buf23
        triton_poi_fused_native_group_norm_relu_15[grid(1048576)](buf90,
            buf91, buf92, primals_22, primals_23, buf95, 1048576, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_23
        buf97 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf99 = reinterpret_tensor(buf97, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf97
        buf100 = empty_strided_cuda((256, 1024, 1, 1), (1024, 1, 1024, 1024
            ), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_16[grid(256)](buf99,
            primals_24, buf100, 256, 1024, num_warps=8, num_stages=1)
        buf101 = extern_kernels.convolution(buf95, buf100, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf101, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf102 = buf92
        del buf92
        buf103 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf105 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf101, buf102,
            buf103, buf105, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf106 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf101,
            buf102, buf103, primals_25, primals_26, buf106, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_26
        buf107 = extern_kernels.convolution(buf106, buf4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf107, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf108 = buf103
        del buf103
        buf109 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf111 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf107, buf108,
            buf109, buf111, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf112 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf107,
            buf108, buf109, primals_28, primals_29, buf112, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_29
        buf114 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf116 = reinterpret_tensor(buf114, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf114
        buf117 = 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)](buf116,
            primals_30, buf117, 1024, 256, num_warps=2, num_stages=1)
        buf118 = extern_kernels.convolution(buf112, buf117, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf118, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf119 = buf118
        del buf118
        triton_poi_fused_add_17[grid(1048576)](buf119, buf90, 1048576,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf120 = buf109
        del buf109
        buf121 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf123 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_14[grid(128)](buf119, buf120,
            buf121, buf123, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf124 = empty_strided_cuda((4, 1024, 16, 16), (262144, 1, 16384, 
            1024), torch.float32)
        triton_poi_fused_native_group_norm_relu_15[grid(1048576)](buf119,
            buf120, buf121, primals_31, primals_32, buf124, 1048576, XBLOCK
            =1024, num_warps=4, num_stages=1)
        del primals_32
        buf126 = empty_strided_cuda((256, 1, 1, 1), (1, 256, 256, 256),
            torch.float32)
        buf128 = reinterpret_tensor(buf126, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf126
        buf129 = empty_strided_cuda((256, 1024, 1, 1), (1024, 1, 1024, 1024
            ), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_16[grid(256)](buf128,
            primals_33, buf129, 256, 1024, num_warps=8, num_stages=1)
        buf130 = extern_kernels.convolution(buf124, buf129, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf130, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf131 = buf121
        del buf121
        buf132 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf134 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf130, buf131,
            buf132, buf134, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf135 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf130,
            buf131, buf132, primals_34, primals_35, buf135, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_35
        buf136 = extern_kernels.convolution(buf135, buf5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf136, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf137 = buf132
        del buf132
        buf138 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf140 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_9[grid(128)](buf136, buf137,
            buf138, buf140, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf141 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_10[grid(262144)](buf136,
            buf137, buf138, primals_37, primals_38, buf141, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_38
        buf143 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf145 = reinterpret_tensor(buf143, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf143
        buf146 = 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)](buf145,
            primals_39, buf146, 1024, 256, num_warps=2, num_stages=1)
        buf147 = extern_kernels.convolution(buf141, buf146, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf147, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf148 = buf147
        del buf147
        triton_poi_fused_add_17[grid(1048576)](buf148, buf119, 1048576,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf149 = buf138
        del buf138
        buf150 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf152 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_14[grid(128)](buf148, buf149,
            buf150, buf152, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf153 = empty_strided_cuda((4, 1024, 16, 16), (262144, 1, 16384, 
            1024), torch.float32)
        triton_poi_fused_native_group_norm_relu_15[grid(1048576)](buf148,
            buf149, buf150, primals_40, primals_41, buf153, 1048576, XBLOCK
            =1024, num_warps=4, num_stages=1)
        del primals_41
        buf155 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf157 = reinterpret_tensor(buf155, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf155
        buf158 = empty_strided_cuda((2048, 1024, 1, 1), (1024, 1, 1024, 
            1024), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_18[grid(2048)](buf157,
            primals_42, buf158, 2048, 1024, num_warps=8, num_stages=1)
        buf159 = extern_kernels.convolution(buf153, buf158, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf159, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf161 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf163 = reinterpret_tensor(buf161, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf161
        buf164 = empty_strided_cuda((512, 1024, 1, 1), (1024, 1, 1024, 1024
            ), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_19[grid(512)](buf163,
            primals_43, buf164, 512, 1024, num_warps=8, num_stages=1)
        buf165 = extern_kernels.convolution(buf153, buf164, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf165, (4, 512, 16, 16), (131072, 1, 8192, 512))
        buf166 = buf150
        del buf150
        buf167 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf169 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_20[grid(128)](buf165, buf166,
            buf167, buf169, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf170 = empty_strided_cuda((4, 512, 16, 16), (131072, 1, 8192, 512
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_21[grid(524288)](buf165,
            buf166, buf167, primals_44, primals_45, buf170, 524288, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_45
        buf171 = extern_kernels.convolution(buf170, buf6, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf171, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf172 = buf167
        del buf167
        buf173 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf175 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_22[grid(128)](buf171, buf172,
            buf173, buf175, 128, 1024, num_warps=8, num_stages=1)
        buf176 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_23[grid(131072)](buf171,
            buf172, buf173, primals_47, primals_48, buf176, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_48
        buf178 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf180 = reinterpret_tensor(buf178, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf178
        buf181 = empty_strided_cuda((2048, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_24[grid(2048)](buf180,
            primals_49, buf181, 2048, 512, num_warps=4, num_stages=1)
        buf182 = extern_kernels.convolution(buf176, buf181, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf182, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf183 = buf159
        del buf159
        triton_poi_fused_add_25[grid(524288)](buf183, buf182, 524288,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf184 = buf173
        del buf173
        buf185 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf187 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_26[grid(128)](buf183, buf184,
            buf185, buf187, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf188 = buf182
        del buf182
        triton_poi_fused_native_group_norm_relu_27[grid(524288)](buf183,
            buf184, buf185, primals_50, primals_51, buf188, 524288, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_51
        buf190 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf192 = reinterpret_tensor(buf190, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf190
        buf193 = empty_strided_cuda((512, 2048, 1, 1), (2048, 1, 2048, 2048
            ), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_28[grid(512)](buf192,
            primals_52, buf193, 512, 2048, XBLOCK=1, RBLOCK=2048, num_warps
            =16, num_stages=1)
        buf194 = extern_kernels.convolution(buf188, buf193, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf194, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf195 = buf185
        del buf185
        buf196 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf198 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_22[grid(128)](buf194, buf195,
            buf196, buf198, 128, 1024, num_warps=8, num_stages=1)
        buf199 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_23[grid(131072)](buf194,
            buf195, buf196, primals_53, primals_54, buf199, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_54
        buf200 = extern_kernels.convolution(buf199, buf7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf200, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf201 = buf196
        del buf196
        buf202 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf204 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_22[grid(128)](buf200, buf201,
            buf202, buf204, 128, 1024, num_warps=8, num_stages=1)
        buf205 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_23[grid(131072)](buf200,
            buf201, buf202, primals_56, primals_57, buf205, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_57
        buf207 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf209 = reinterpret_tensor(buf207, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf207
        buf210 = empty_strided_cuda((2048, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_24[grid(2048)](buf209,
            primals_58, buf210, 2048, 512, num_warps=4, num_stages=1)
        buf211 = extern_kernels.convolution(buf205, buf210, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf211, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf212 = buf211
        del buf211
        triton_poi_fused_add_29[grid(524288)](buf212, buf183, 524288,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf213 = buf202
        del buf202
        buf214 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf216 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_26[grid(128)](buf212, buf213,
            buf214, buf216, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf217 = empty_strided_cuda((4, 2048, 8, 8), (131072, 1, 16384, 
            2048), torch.float32)
        triton_poi_fused_native_group_norm_relu_27[grid(524288)](buf212,
            buf213, buf214, primals_59, primals_60, buf217, 524288, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_60
        buf219 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf221 = reinterpret_tensor(buf219, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf219
        buf222 = empty_strided_cuda((512, 2048, 1, 1), (2048, 1, 2048, 2048
            ), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_28[grid(512)](buf221,
            primals_61, buf222, 512, 2048, XBLOCK=1, RBLOCK=2048, num_warps
            =16, num_stages=1)
        buf223 = extern_kernels.convolution(buf217, buf222, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf223, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf224 = buf214
        del buf214
        buf225 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf227 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_22[grid(128)](buf223, buf224,
            buf225, buf227, 128, 1024, num_warps=8, num_stages=1)
        buf228 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_23[grid(131072)](buf223,
            buf224, buf225, primals_62, primals_63, buf228, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_63
        buf229 = extern_kernels.convolution(buf228, buf8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf229, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf230 = buf225
        del buf225
        buf231 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf233 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_22[grid(128)](buf229, buf230,
            buf231, buf233, 128, 1024, num_warps=8, num_stages=1)
        buf234 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_23[grid(131072)](buf229,
            buf230, buf231, primals_65, primals_66, buf234, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_66
        buf236 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf238 = reinterpret_tensor(buf236, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf236
        buf239 = empty_strided_cuda((2048, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_24[grid(2048)](buf238,
            primals_67, buf239, 2048, 512, num_warps=4, num_stages=1)
        buf240 = extern_kernels.convolution(buf234, buf239, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf240, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf241 = buf240
        del buf240
        triton_poi_fused_add_29[grid(524288)](buf241, buf212, 524288,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf242 = buf231
        del buf231
        buf243 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf245 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_26[grid(128)](buf241, buf242,
            buf243, buf245, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf246 = empty_strided_cuda((4, 2048, 8, 8), (131072, 1, 16384, 
            2048), torch.float32)
        triton_poi_fused_native_group_norm_relu_27[grid(524288)](buf241,
            buf242, buf243, primals_68, primals_69, buf246, 524288, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_69
        buf248 = empty_strided_cuda((512, 1, 1, 1), (1, 512, 512, 512),
            torch.float32)
        buf250 = reinterpret_tensor(buf248, (512, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf248
        buf251 = empty_strided_cuda((512, 2048, 1, 1), (2048, 1, 2048, 2048
            ), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_28[grid(512)](buf250,
            primals_70, buf251, 512, 2048, XBLOCK=1, RBLOCK=2048, num_warps
            =16, num_stages=1)
        buf252 = extern_kernels.convolution(buf246, buf251, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf252, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf253 = buf243
        del buf243
        buf254 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf256 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_22[grid(128)](buf252, buf253,
            buf254, buf256, 128, 1024, num_warps=8, num_stages=1)
        buf257 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_23[grid(131072)](buf252,
            buf253, buf254, primals_71, primals_72, buf257, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_72
        buf258 = extern_kernels.convolution(buf257, buf9, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf258, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf259 = buf254
        del buf254
        buf260 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf262 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_22[grid(128)](buf258, buf259,
            buf260, buf262, 128, 1024, num_warps=8, num_stages=1)
        buf263 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_23[grid(131072)](buf258,
            buf259, buf260, primals_74, primals_75, buf263, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_75
        buf265 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf267 = reinterpret_tensor(buf265, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf265
        buf268 = empty_strided_cuda((2048, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_24[grid(2048)](buf267,
            primals_76, buf268, 2048, 512, num_warps=4, num_stages=1)
        buf269 = extern_kernels.convolution(buf263, buf268, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf269, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf270 = buf269
        del buf269
        triton_poi_fused_add_29[grid(524288)](buf270, buf241, 524288,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf271 = buf260
        del buf260
        buf272 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf274 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_26[grid(128)](buf270, buf271,
            buf272, buf274, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf275 = empty_strided_cuda((4, 2048, 8, 8), (131072, 1, 16384, 
            2048), torch.float32)
        triton_poi_fused_native_group_norm_relu_27[grid(524288)](buf270,
            buf271, buf272, primals_77, primals_78, buf275, 524288, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_78
        buf277 = empty_strided_cuda((4096, 1, 1, 1), (1, 4096, 4096, 4096),
            torch.float32)
        buf279 = reinterpret_tensor(buf277, (4096, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf277
        buf280 = empty_strided_cuda((4096, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_30[grid(4096)](buf279,
            primals_79, buf280, 4096, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf281 = extern_kernels.convolution(buf275, buf280, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf281, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf283 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf285 = reinterpret_tensor(buf283, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf283
        buf286 = empty_strided_cuda((1024, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_31[grid(1024)](buf285,
            primals_80, buf286, 1024, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf287 = extern_kernels.convolution(buf275, 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, 1024, 8, 8), (65536, 1, 8192, 1024))
        buf288 = buf272
        del buf272
        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_red_fused_native_group_norm_32[grid(128)](buf287, buf288,
            buf289, buf291, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf292 = empty_strided_cuda((4, 1024, 8, 8), (65536, 1, 8192, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_33[grid(262144)](buf287,
            buf288, buf289, primals_81, primals_82, buf292, 262144, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_82
        buf293 = extern_kernels.convolution(buf292, buf10, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf293, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf294 = buf289
        del buf289
        buf295 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf297 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_34[grid(128)](buf293, buf294,
            buf295, buf297, 128, 512, num_warps=4, num_stages=1)
        buf298 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_35[grid(65536)](buf293,
            buf294, buf295, primals_84, primals_85, buf298, 65536, XBLOCK=
            512, num_warps=4, num_stages=1)
        del primals_85
        buf300 = empty_strided_cuda((4096, 1, 1, 1), (1, 4096, 4096, 4096),
            torch.float32)
        buf302 = reinterpret_tensor(buf300, (4096, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf300
        buf303 = empty_strided_cuda((4096, 1024, 1, 1), (1024, 1, 1024, 
            1024), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_36[grid(4096)](buf302,
            primals_86, buf303, 4096, 1024, num_warps=8, num_stages=1)
        buf304 = extern_kernels.convolution(buf298, buf303, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf304, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf305 = buf281
        del buf281
        triton_poi_fused_add_37[grid(262144)](buf305, buf304, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf306 = buf295
        del buf295
        buf307 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf309 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_38[grid(128)](buf305, buf306,
            buf307, buf309, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf310 = buf304
        del buf304
        triton_poi_fused_native_group_norm_relu_39[grid(262144)](buf305,
            buf306, buf307, primals_87, primals_88, buf310, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_88
        buf312 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf314 = reinterpret_tensor(buf312, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf312
        buf315 = empty_strided_cuda((1024, 4096, 1, 1), (4096, 1, 4096, 
            4096), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_40[grid(1024)](buf314,
            primals_89, buf315, 1024, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf316 = extern_kernels.convolution(buf310, buf315, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf316, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf317 = buf307
        del buf307
        buf318 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf320 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_34[grid(128)](buf316, buf317,
            buf318, buf320, 128, 512, num_warps=4, num_stages=1)
        buf321 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_35[grid(65536)](buf316,
            buf317, buf318, primals_90, primals_91, buf321, 65536, XBLOCK=
            512, num_warps=4, num_stages=1)
        del primals_91
        buf322 = extern_kernels.convolution(buf321, buf11, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf322, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf323 = buf318
        del buf318
        buf324 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf326 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_34[grid(128)](buf322, buf323,
            buf324, buf326, 128, 512, num_warps=4, num_stages=1)
        buf327 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_35[grid(65536)](buf322,
            buf323, buf324, primals_93, primals_94, buf327, 65536, XBLOCK=
            512, num_warps=4, num_stages=1)
        del primals_94
        buf329 = empty_strided_cuda((4096, 1, 1, 1), (1, 4096, 4096, 4096),
            torch.float32)
        buf331 = reinterpret_tensor(buf329, (4096, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf329
        buf332 = empty_strided_cuda((4096, 1024, 1, 1), (1024, 1, 1024, 
            1024), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_36[grid(4096)](buf331,
            primals_95, buf332, 4096, 1024, num_warps=8, num_stages=1)
        buf333 = extern_kernels.convolution(buf327, buf332, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf333, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf334 = buf333
        del buf333
        triton_poi_fused_add_41[grid(262144)](buf334, buf305, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf335 = buf324
        del buf324
        buf336 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf338 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_38[grid(128)](buf334, buf335,
            buf336, buf338, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf339 = empty_strided_cuda((4, 4096, 4, 4), (65536, 1, 16384, 4096
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_39[grid(262144)](buf334,
            buf335, buf336, primals_96, primals_97, buf339, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_97
        buf341 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf343 = reinterpret_tensor(buf341, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf341
        buf344 = empty_strided_cuda((1024, 4096, 1, 1), (4096, 1, 4096, 
            4096), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_40[grid(1024)](buf343,
            primals_98, buf344, 1024, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf345 = extern_kernels.convolution(buf339, buf344, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf345, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf346 = buf336
        del buf336
        buf347 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf349 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_34[grid(128)](buf345, buf346,
            buf347, buf349, 128, 512, num_warps=4, num_stages=1)
        buf350 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_35[grid(65536)](buf345,
            buf346, buf347, primals_99, primals_100, buf350, 65536, XBLOCK=
            512, num_warps=4, num_stages=1)
        del primals_100
        buf351 = extern_kernels.convolution(buf350, buf12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf351, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf352 = buf347
        del buf347
        buf353 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf355 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_34[grid(128)](buf351, buf352,
            buf353, buf355, 128, 512, num_warps=4, num_stages=1)
        buf356 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_35[grid(65536)](buf351,
            buf352, buf353, primals_102, primals_103, buf356, 65536, XBLOCK
            =512, num_warps=4, num_stages=1)
        del primals_103
        buf358 = empty_strided_cuda((4096, 1, 1, 1), (1, 4096, 4096, 4096),
            torch.float32)
        buf360 = reinterpret_tensor(buf358, (4096, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf358
        buf361 = empty_strided_cuda((4096, 1024, 1, 1), (1024, 1, 1024, 
            1024), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_36[grid(4096)](buf360,
            primals_104, buf361, 4096, 1024, num_warps=8, num_stages=1)
        buf362 = extern_kernels.convolution(buf356, buf361, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf362, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf363 = buf362
        del buf362
        triton_poi_fused_add_41[grid(262144)](buf363, buf334, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf364 = buf353
        del buf353
        buf365 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf367 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_38[grid(128)](buf363, buf364,
            buf365, buf367, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf368 = empty_strided_cuda((4, 4096, 4, 4), (65536, 1, 16384, 4096
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_39[grid(262144)](buf363,
            buf364, buf365, primals_105, primals_106, buf368, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_106
        buf370 = empty_strided_cuda((1024, 1, 1, 1), (1, 1024, 1024, 1024),
            torch.float32)
        buf372 = reinterpret_tensor(buf370, (1024, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf370
        buf373 = empty_strided_cuda((1024, 4096, 1, 1), (4096, 1, 4096, 
            4096), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_40[grid(1024)](buf372,
            primals_107, buf373, 1024, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf374 = extern_kernels.convolution(buf368, buf373, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf374, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf375 = buf365
        del buf365
        buf376 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf378 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_34[grid(128)](buf374, buf375,
            buf376, buf378, 128, 512, num_warps=4, num_stages=1)
        buf379 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_35[grid(65536)](buf374,
            buf375, buf376, primals_108, primals_109, buf379, 65536, XBLOCK
            =512, num_warps=4, num_stages=1)
        del primals_109
        buf380 = extern_kernels.convolution(buf379, buf13, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf380, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf381 = buf376
        del buf376
        buf382 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf384 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_34[grid(128)](buf380, buf381,
            buf382, buf384, 128, 512, num_warps=4, num_stages=1)
        buf385 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_35[grid(65536)](buf380,
            buf381, buf382, primals_111, primals_112, buf385, 65536, XBLOCK
            =512, num_warps=4, num_stages=1)
        del primals_112
        buf387 = empty_strided_cuda((4096, 1, 1, 1), (1, 4096, 4096, 4096),
            torch.float32)
        buf389 = reinterpret_tensor(buf387, (4096, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf387
        buf390 = empty_strided_cuda((4096, 1024, 1, 1), (1024, 1, 1024, 
            1024), torch.float32)
        triton_per_fused_add_div_sqrt_sub_var_mean_36[grid(4096)](buf389,
            primals_113, buf390, 4096, 1024, num_warps=8, num_stages=1)
        buf391 = extern_kernels.convolution(buf385, buf390, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf391, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf392 = buf391
        del buf391
        triton_poi_fused_add_41[grid(262144)](buf392, buf363, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf393 = buf382
        del buf382
        buf394 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf396 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_38[grid(128)](buf392, buf393,
            buf394, buf396, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf397 = empty_strided_cuda((4, 4096, 4, 4), (65536, 1, 16384, 4096
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_39[grid(262144)](buf392,
            buf393, buf394, primals_114, primals_115, buf397, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_115
        buf399 = empty_strided_cuda((8192, 1, 1, 1), (1, 8192, 8192, 8192),
            torch.float32)
        buf401 = reinterpret_tensor(buf399, (8192, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf399
        buf402 = empty_strided_cuda((8192, 4096, 1, 1), (4096, 1, 4096, 
            4096), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_42[grid(8192)](buf401,
            primals_116, buf402, 8192, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf403 = extern_kernels.convolution(buf397, buf402, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf403, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf405 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf407 = reinterpret_tensor(buf405, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf405
        buf408 = empty_strided_cuda((2048, 4096, 1, 1), (4096, 1, 4096, 
            4096), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_43[grid(2048)](buf407,
            primals_117, buf408, 2048, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf409 = extern_kernels.convolution(buf397, buf408, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf409, (4, 2048, 4, 4), (32768, 1, 8192, 2048))
        buf410 = buf394
        del buf394
        buf411 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf413 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_44[grid(128)](buf409, buf410,
            buf411, buf413, 128, 1024, num_warps=8, num_stages=1)
        buf414 = empty_strided_cuda((4, 2048, 4, 4), (32768, 1, 8192, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_45[grid(131072)](buf409,
            buf410, buf411, primals_118, primals_119, buf414, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_119
        buf415 = extern_kernels.convolution(buf414, buf14, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf415, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf416 = buf411
        del buf411
        buf417 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf419 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_46[grid(128)](buf415, buf416,
            buf417, buf419, 128, 256, num_warps=2, num_stages=1)
        buf420 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_47[grid(32768)](buf415,
            buf416, buf417, primals_121, primals_122, buf420, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_122
        buf422 = empty_strided_cuda((8192, 1, 1, 1), (1, 8192, 8192, 8192),
            torch.float32)
        buf424 = reinterpret_tensor(buf422, (8192, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf422
        buf425 = empty_strided_cuda((8192, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_48[grid(8192)](buf424,
            primals_123, buf425, 8192, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf426 = extern_kernels.convolution(buf420, buf425, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf426, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf427 = buf403
        del buf403
        triton_poi_fused_add_49[grid(131072)](buf427, buf426, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf428 = buf417
        del buf417
        buf429 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf431 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_50[grid(128)](buf427, buf428,
            buf429, buf431, 128, 1024, num_warps=8, num_stages=1)
        buf432 = buf426
        del buf426
        triton_poi_fused_native_group_norm_relu_51[grid(131072)](buf427,
            buf428, buf429, primals_124, primals_125, buf432, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_125
        buf434 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf436 = reinterpret_tensor(buf434, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf434
        buf437 = empty_strided_cuda((2048, 8192, 1, 1), (8192, 1, 8192, 
            8192), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_52[grid(2048)](buf436,
            primals_126, buf437, 2048, 8192, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf438 = extern_kernels.convolution(buf432, buf437, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf438, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf439 = buf429
        del buf429
        buf440 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf442 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_46[grid(128)](buf438, buf439,
            buf440, buf442, 128, 256, num_warps=2, num_stages=1)
        buf443 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_47[grid(32768)](buf438,
            buf439, buf440, primals_127, primals_128, buf443, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_128
        buf444 = extern_kernels.convolution(buf443, buf15, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf444, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf445 = buf440
        del buf440
        buf446 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf448 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_46[grid(128)](buf444, buf445,
            buf446, buf448, 128, 256, num_warps=2, num_stages=1)
        buf449 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_47[grid(32768)](buf444,
            buf445, buf446, primals_130, primals_131, buf449, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_131
        buf451 = empty_strided_cuda((8192, 1, 1, 1), (1, 8192, 8192, 8192),
            torch.float32)
        buf453 = reinterpret_tensor(buf451, (8192, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf451
        buf454 = empty_strided_cuda((8192, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_48[grid(8192)](buf453,
            primals_132, buf454, 8192, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf455 = extern_kernels.convolution(buf449, buf454, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf455, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf456 = buf455
        del buf455
        triton_poi_fused_add_53[grid(131072)](buf456, buf427, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf457 = buf446
        del buf446
        buf458 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf460 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_50[grid(128)](buf456, buf457,
            buf458, buf460, 128, 1024, num_warps=8, num_stages=1)
        buf461 = empty_strided_cuda((4, 8192, 2, 2), (32768, 1, 16384, 8192
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_51[grid(131072)](buf456,
            buf457, buf458, primals_133, primals_134, buf461, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_134
        buf463 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf465 = reinterpret_tensor(buf463, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf463
        buf466 = empty_strided_cuda((2048, 8192, 1, 1), (8192, 1, 8192, 
            8192), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_52[grid(2048)](buf465,
            primals_135, buf466, 2048, 8192, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf467 = extern_kernels.convolution(buf461, buf466, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf467, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf468 = buf458
        del buf458
        buf469 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf471 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_46[grid(128)](buf467, buf468,
            buf469, buf471, 128, 256, num_warps=2, num_stages=1)
        buf472 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_47[grid(32768)](buf467,
            buf468, buf469, primals_136, primals_137, buf472, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_137
        buf473 = extern_kernels.convolution(buf472, buf16, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf473, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf474 = buf469
        del buf469
        buf475 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf477 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_46[grid(128)](buf473, buf474,
            buf475, buf477, 128, 256, num_warps=2, num_stages=1)
        buf478 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_47[grid(32768)](buf473,
            buf474, buf475, primals_139, primals_140, buf478, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_140
        buf480 = empty_strided_cuda((8192, 1, 1, 1), (1, 8192, 8192, 8192),
            torch.float32)
        buf482 = reinterpret_tensor(buf480, (8192, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf480
        buf483 = empty_strided_cuda((8192, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_48[grid(8192)](buf482,
            primals_141, buf483, 8192, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf484 = extern_kernels.convolution(buf478, buf483, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf484, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf485 = buf484
        del buf484
        triton_poi_fused_add_53[grid(131072)](buf485, buf456, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf486 = buf475
        del buf475
        buf487 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf489 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_50[grid(128)](buf485, buf486,
            buf487, buf489, 128, 1024, num_warps=8, num_stages=1)
        buf490 = empty_strided_cuda((4, 8192, 2, 2), (32768, 1, 16384, 8192
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_51[grid(131072)](buf485,
            buf486, buf487, primals_142, primals_143, buf490, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_143
        buf492 = empty_strided_cuda((2048, 1, 1, 1), (1, 2048, 2048, 2048),
            torch.float32)
        buf494 = reinterpret_tensor(buf492, (2048, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf492
        buf495 = empty_strided_cuda((2048, 8192, 1, 1), (8192, 1, 8192, 
            8192), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_52[grid(2048)](buf494,
            primals_144, buf495, 2048, 8192, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf496 = extern_kernels.convolution(buf490, buf495, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf496, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf497 = buf487
        del buf487
        buf498 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf500 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_46[grid(128)](buf496, buf497,
            buf498, buf500, 128, 256, num_warps=2, num_stages=1)
        buf501 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_47[grid(32768)](buf496,
            buf497, buf498, primals_145, primals_146, buf501, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_146
        buf502 = extern_kernels.convolution(buf501, buf17, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf502, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf503 = buf498
        del buf498
        buf504 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf506 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_46[grid(128)](buf502, buf503,
            buf504, buf506, 128, 256, num_warps=2, num_stages=1)
        buf507 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_47[grid(32768)](buf502,
            buf503, buf504, primals_148, primals_149, buf507, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_149
        buf509 = empty_strided_cuda((8192, 1, 1, 1), (1, 8192, 8192, 8192),
            torch.float32)
        buf511 = reinterpret_tensor(buf509, (8192, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf509
        buf512 = empty_strided_cuda((8192, 2048, 1, 1), (2048, 1, 2048, 
            2048), torch.float32)
        triton_red_fused_add_div_sqrt_sub_var_mean_48[grid(8192)](buf511,
            primals_150, buf512, 8192, 2048, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        buf513 = extern_kernels.convolution(buf507, buf512, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf513, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf514 = buf513
        del buf513
        triton_poi_fused_add_53[grid(131072)](buf514, buf485, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf515 = reinterpret_tensor(buf504, (4, 32, 1, 1), (32, 1, 32, 32), 0)
        del buf504
        buf516 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf518 = reinterpret_tensor(buf516, (4, 32, 1, 1), (32, 1, 32, 32), 0)
        del buf516
        triton_per_fused_native_group_norm_54[grid(128)](buf518, buf514,
            buf515, 128, 1024, num_warps=8, num_stages=1)
        buf519 = empty_strided_cuda((4, 8192, 1, 1), (8192, 1, 8192, 8192),
            torch.float32)
        triton_poi_fused_mean_native_group_norm_relu_55[grid(32768)](buf514,
            buf515, buf518, primals_151, primals_152, buf519, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf520 = extern_kernels.convolution(buf519, primals_153, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf520, (4, 21843, 1, 1), (21843, 1, 21843, 21843))
        buf521 = reinterpret_tensor(buf520, (4, 21843, 1, 1), (21843, 1, 
            87372, 87372), 0)
        del buf520
        triton_poi_fused_convolution_56[grid(87372)](buf521, primals_154, 
            87372, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_154
    return (reinterpret_tensor(buf521, (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, buf49,
        reinterpret_tensor(buf50, (4, 32), (32, 1), 0), reinterpret_tensor(
        buf53, (4, 32), (32, 1), 0), buf54, buf58, buf59, buf61,
        reinterpret_tensor(buf62, (4, 32), (32, 1), 0), reinterpret_tensor(
        buf65, (4, 32), (32, 1), 0), buf66, buf70, buf71, buf72,
        reinterpret_tensor(buf73, (4, 32), (32, 1), 0), reinterpret_tensor(
        buf76, (4, 32), (32, 1), 0), buf77, buf78, reinterpret_tensor(buf79,
        (4, 32), (32, 1), 0), reinterpret_tensor(buf82, (4, 32), (32, 1), 0
        ), buf83, buf87, buf88, buf90, reinterpret_tensor(buf91, (4, 32), (
        32, 1), 0), reinterpret_tensor(buf94, (4, 32), (32, 1), 0), buf95,
        buf99, buf100, buf101, reinterpret_tensor(buf102, (4, 32), (32, 1),
        0), reinterpret_tensor(buf105, (4, 32), (32, 1), 0), buf106, buf107,
        reinterpret_tensor(buf108, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf111, (4, 32), (32, 1), 0), buf112, buf116, buf117, buf119,
        reinterpret_tensor(buf120, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf123, (4, 32), (32, 1), 0), buf124, buf128, buf129, buf130,
        reinterpret_tensor(buf131, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf134, (4, 32), (32, 1), 0), buf135, buf136, reinterpret_tensor(
        buf137, (4, 32), (32, 1), 0), reinterpret_tensor(buf140, (4, 32), (
        32, 1), 0), buf141, buf145, buf146, buf148, reinterpret_tensor(
        buf149, (4, 32), (32, 1), 0), reinterpret_tensor(buf152, (4, 32), (
        32, 1), 0), buf153, buf157, buf158, buf163, buf164, buf165,
        reinterpret_tensor(buf166, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf169, (4, 32), (32, 1), 0), buf170, buf171, reinterpret_tensor(
        buf172, (4, 32), (32, 1), 0), reinterpret_tensor(buf175, (4, 32), (
        32, 1), 0), buf176, buf180, buf181, buf183, reinterpret_tensor(
        buf184, (4, 32), (32, 1), 0), reinterpret_tensor(buf187, (4, 32), (
        32, 1), 0), buf188, buf192, buf193, buf194, reinterpret_tensor(
        buf195, (4, 32), (32, 1), 0), reinterpret_tensor(buf198, (4, 32), (
        32, 1), 0), buf199, buf200, reinterpret_tensor(buf201, (4, 32), (32,
        1), 0), reinterpret_tensor(buf204, (4, 32), (32, 1), 0), buf205,
        buf209, buf210, buf212, reinterpret_tensor(buf213, (4, 32), (32, 1),
        0), reinterpret_tensor(buf216, (4, 32), (32, 1), 0), buf217, buf221,
        buf222, buf223, reinterpret_tensor(buf224, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf227, (4, 32), (32, 1), 0), buf228, buf229,
        reinterpret_tensor(buf230, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf233, (4, 32), (32, 1), 0), buf234, buf238, buf239, buf241,
        reinterpret_tensor(buf242, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf245, (4, 32), (32, 1), 0), buf246, buf250, buf251, buf252,
        reinterpret_tensor(buf253, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf256, (4, 32), (32, 1), 0), buf257, buf258, reinterpret_tensor(
        buf259, (4, 32), (32, 1), 0), reinterpret_tensor(buf262, (4, 32), (
        32, 1), 0), buf263, buf267, buf268, buf270, reinterpret_tensor(
        buf271, (4, 32), (32, 1), 0), reinterpret_tensor(buf274, (4, 32), (
        32, 1), 0), buf275, buf279, buf280, buf285, buf286, buf287,
        reinterpret_tensor(buf288, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf291, (4, 32), (32, 1), 0), buf292, buf293, reinterpret_tensor(
        buf294, (4, 32), (32, 1), 0), reinterpret_tensor(buf297, (4, 32), (
        32, 1), 0), buf298, buf302, buf303, buf305, reinterpret_tensor(
        buf306, (4, 32), (32, 1), 0), reinterpret_tensor(buf309, (4, 32), (
        32, 1), 0), buf310, buf314, buf315, buf316, reinterpret_tensor(
        buf317, (4, 32), (32, 1), 0), reinterpret_tensor(buf320, (4, 32), (
        32, 1), 0), buf321, buf322, reinterpret_tensor(buf323, (4, 32), (32,
        1), 0), reinterpret_tensor(buf326, (4, 32), (32, 1), 0), buf327,
        buf331, buf332, buf334, reinterpret_tensor(buf335, (4, 32), (32, 1),
        0), reinterpret_tensor(buf338, (4, 32), (32, 1), 0), buf339, buf343,
        buf344, buf345, reinterpret_tensor(buf346, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf349, (4, 32), (32, 1), 0), buf350, buf351,
        reinterpret_tensor(buf352, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf355, (4, 32), (32, 1), 0), buf356, buf360, buf361, buf363,
        reinterpret_tensor(buf364, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf367, (4, 32), (32, 1), 0), buf368, buf372, buf373, buf374,
        reinterpret_tensor(buf375, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf378, (4, 32), (32, 1), 0), buf379, buf380, reinterpret_tensor(
        buf381, (4, 32), (32, 1), 0), reinterpret_tensor(buf384, (4, 32), (
        32, 1), 0), buf385, buf389, buf390, buf392, reinterpret_tensor(
        buf393, (4, 32), (32, 1), 0), reinterpret_tensor(buf396, (4, 32), (
        32, 1), 0), buf397, buf401, buf402, buf407, buf408, buf409,
        reinterpret_tensor(buf410, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf413, (4, 32), (32, 1), 0), buf414, buf415, reinterpret_tensor(
        buf416, (4, 32), (32, 1), 0), reinterpret_tensor(buf419, (4, 32), (
        32, 1), 0), buf420, buf424, buf425, buf427, reinterpret_tensor(
        buf428, (4, 32), (32, 1), 0), reinterpret_tensor(buf431, (4, 32), (
        32, 1), 0), buf432, buf436, buf437, buf438, reinterpret_tensor(
        buf439, (4, 32), (32, 1), 0), reinterpret_tensor(buf442, (4, 32), (
        32, 1), 0), buf443, buf444, reinterpret_tensor(buf445, (4, 32), (32,
        1), 0), reinterpret_tensor(buf448, (4, 32), (32, 1), 0), buf449,
        buf453, buf454, buf456, reinterpret_tensor(buf457, (4, 32), (32, 1),
        0), reinterpret_tensor(buf460, (4, 32), (32, 1), 0), buf461, buf465,
        buf466, buf467, reinterpret_tensor(buf468, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf471, (4, 32), (32, 1), 0), buf472, buf473,
        reinterpret_tensor(buf474, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf477, (4, 32), (32, 1), 0), buf478, buf482, buf483, buf485,
        reinterpret_tensor(buf486, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf489, (4, 32), (32, 1), 0), buf490, buf494, buf495, buf496,
        reinterpret_tensor(buf497, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf500, (4, 32), (32, 1), 0), buf501, buf502, reinterpret_tensor(
        buf503, (4, 32), (32, 1), 0), reinterpret_tensor(buf506, (4, 32), (
        32, 1), 0), buf507, buf511, buf512, buf514, buf515, buf518, buf519)


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


def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
        padding=1, bias=False)


def tf2th(conv_weights):
    """Possibly convert HWIO to OIHW."""
    if conv_weights.ndim == 4:
        conv_weights = conv_weights.transpose([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):
    """Pre-activation (v2) bottleneck block.

  Follows the implementation of "Identity Mappings in Deep Residual Networks":
  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=''):
        convname = 'standardized_conv2d'
        with torch.no_grad():
            self.conv1.weight.copy_(tf2th(weights[
                f'{prefix}a/{convname}/kernel']))
            self.conv2.weight.copy_(tf2th(weights[
                f'{prefix}b/{convname}/kernel']))
            self.conv3.weight.copy_(tf2th(weights[
                f'{prefix}c/{convname}/kernel']))
            self.gn1.weight.copy_(tf2th(weights[f'{prefix}a/group_norm/gamma'])
                )
            self.gn2.weight.copy_(tf2th(weights[f'{prefix}b/group_norm/gamma'])
                )
            self.gn3.weight.copy_(tf2th(weights[f'{prefix}c/group_norm/gamma'])
                )
            self.gn1.bias.copy_(tf2th(weights[f'{prefix}a/group_norm/beta']))
            self.gn2.bias.copy_(tf2th(weights[f'{prefix}b/group_norm/beta']))
            self.gn3.bias.copy_(tf2th(weights[f'{prefix}c/group_norm/beta']))
            if hasattr(self, 'downsample'):
                w = weights[f'{prefix}a/proj/{convname}/kernel']
                self.downsample.weight.copy_(tf2th(w))


class ResNetV2New(nn.Module):
    """Implementation of Pre-activation (v2) ResNet mode."""

    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)), ('pad',
            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}/')

    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: 0.014109