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
from collections import OrderedDict
import torch.utils.data
import torchvision.transforms.functional as F
from torch.nn import functional as F
from collections.__init__ 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_mean_mul_sqrt_std_sub_6(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, 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])
    tmp3 = 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]
    tmp18 = tl.sum(tmp3, 1)[:, None]
    tmp19 = 146.0
    tmp20 = tmp16 / tmp19
    tmp21 = libdevice.sqrt(tmp20)
    tmp22 = 147.0
    tmp23 = tmp18 / tmp22
    tmp24 = tmp0 - tmp23
    tmp25 = tmp21 * tmp21
    tmp26 = 1e-10
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = tmp24 / tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp23, xmask)
    tl.store(out_ptr0 + (r1 + 147 * x0), tmp29, rmask & xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_7(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)
    x2 = xindex // 4096 % 16
    x1 = xindex // 256 % 16
    x0 = xindex % 256
    x5 = xindex // 4096
    x6 = xindex
    tmp0 = -1 + 2 * x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 32, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + 2 * x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-8448 + x0 + 512 * x1 + 16384 * x5), tmp10,
        other=float('-inf'))
    tmp12 = 2 * x1
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-8192 + x0 + 512 * x1 + 16384 * x5), tmp16,
        other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (-7936 + x0 + 512 * x1 + 16384 * x5), tmp23,
        other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 2 * x2
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (-256 + x0 + 512 * x1 + 16384 * x5), tmp30,
        other=float('-inf'))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x0 + 512 * x1 + 16384 * x5), tmp33, other=
        float('-inf'))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (256 + x0 + 512 * x1 + 16384 * x5), tmp36,
        other=float('-inf'))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + 2 * x2
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (7936 + x0 + 512 * x1 + 16384 * x5), tmp43,
        other=float('-inf'))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (8192 + x0 + 512 * x1 + 16384 * x5), tmp46,
        other=float('-inf'))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (8448 + x0 + 512 * x1 + 16384 * x5), tmp49,
        other=float('-inf'))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp52 = tmp17 > tmp11
    tmp53 = tl.full([1], 1, tl.int8)
    tmp54 = tl.full([1], 0, tl.int8)
    tmp55 = tl.where(tmp52, tmp53, tmp54)
    tmp56 = tmp24 > tmp18
    tmp57 = tl.full([1], 2, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp31 > tmp25
    tmp60 = tl.full([1], 3, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp34 > tmp32
    tmp63 = tl.full([1], 4, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp37 > tmp35
    tmp66 = tl.full([1], 5, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp44 > tmp38
    tmp69 = tl.full([1], 6, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp47 > tmp45
    tmp72 = tl.full([1], 7, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp50 > tmp48
    tmp75 = tl.full([1], 8, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + x6, tmp51, None)
    tl.store(out_ptr1 + x6, tmp76, None)


@triton.jit
def triton_red_fused_native_group_norm_8(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_9(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_poi_fused_add_10(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_11(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_12(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_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_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_native_group_norm_14(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_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 % 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_16(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_17(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_poi_fused_add_18(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_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_19(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_20(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_poi_fused_add_21(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_native_group_norm_22(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_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 % 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_24(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_25(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 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_poi_fused_add_26(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_27(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_28(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_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_per_fused_native_group_norm_30(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_31(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_32(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_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 % 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_poi_fused_add_34(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_35(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_36(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_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_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_38(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_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)
    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_40(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)
        buf22 = 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
        buf23 = reinterpret_tensor(buf22, (256, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf22
        buf24 = empty_strided_cuda((256, 3, 7, 7), (147, 1, 21, 3), torch.
            float32)
        triton_per_fused_add_div_mean_mul_sqrt_std_sub_6[grid(256)](buf21,
            buf23, buf0, buf24, 256, 147, XBLOCK=1, num_warps=2, num_stages=1)
        buf25 = extern_kernels.convolution(buf1, buf24, stride=(2, 2),
            padding=(3, 3), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf25, (4, 256, 32, 32), (262144, 1, 8192, 256))
        buf26 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        buf27 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_7[grid(262144)](buf25,
            buf26, buf27, 262144, XBLOCK=512, num_warps=8, num_stages=1)
        buf28 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf29 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf31 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_8[grid(128)](buf26, buf28, buf29,
            buf31, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf32 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_9[grid(262144)](buf26,
            buf28, buf29, primals_3, primals_4, buf32, 262144, XBLOCK=1024,
            num_warps=4, num_stages=1)
        del primals_4
        buf33 = extern_kernels.convolution(buf32, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf34 = extern_kernels.convolution(buf32, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf34, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf35 = buf29
        del buf29
        buf36 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf38 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_8[grid(128)](buf34, buf35, buf36,
            buf38, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf39 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_9[grid(262144)](buf34,
            buf35, buf36, primals_7, primals_8, buf39, 262144, XBLOCK=1024,
            num_warps=4, num_stages=1)
        del primals_8
        buf40 = extern_kernels.convolution(buf39, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf41 = buf36
        del buf36
        buf42 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf44 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_8[grid(128)](buf40, buf41, buf42,
            buf44, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf45 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_9[grid(262144)](buf40,
            buf41, buf42, primals_10, primals_11, buf45, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_11
        buf46 = extern_kernels.convolution(buf45, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf46, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf47 = buf33
        del buf33
        triton_poi_fused_add_10[grid(1048576)](buf47, buf46, 1048576,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf48 = buf42
        del buf42
        buf49 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf51 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_11[grid(128)](buf47, buf48,
            buf49, buf51, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf52 = buf46
        del buf46
        triton_poi_fused_native_group_norm_relu_12[grid(1048576)](buf47,
            buf48, buf49, primals_13, primals_14, buf52, 1048576, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_14
        buf53 = extern_kernels.convolution(buf52, primals_15, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf53, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf54 = buf49
        del buf49
        buf55 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf57 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_8[grid(128)](buf53, buf54, buf55,
            buf57, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf58 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_9[grid(262144)](buf53,
            buf54, buf55, primals_16, primals_17, buf58, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_17
        buf59 = extern_kernels.convolution(buf58, buf3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf59, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf60 = buf55
        del buf55
        buf61 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf63 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_8[grid(128)](buf59, buf60, buf61,
            buf63, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf64 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_9[grid(262144)](buf59,
            buf60, buf61, primals_19, primals_20, buf64, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_20
        buf65 = extern_kernels.convolution(buf64, primals_21, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf65, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf66 = buf65
        del buf65
        triton_poi_fused_add_13[grid(1048576)](buf66, buf47, 1048576,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf67 = buf61
        del buf61
        buf68 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf70 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_11[grid(128)](buf66, buf67,
            buf68, buf70, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf71 = empty_strided_cuda((4, 1024, 16, 16), (262144, 1, 16384, 
            1024), torch.float32)
        triton_poi_fused_native_group_norm_relu_12[grid(1048576)](buf66,
            buf67, buf68, primals_22, primals_23, buf71, 1048576, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_23
        buf72 = extern_kernels.convolution(buf71, primals_24, 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 = buf68
        del buf68
        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_8[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_9[grid(262144)](buf72,
            buf73, buf74, primals_25, primals_26, buf77, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_26
        buf78 = extern_kernels.convolution(buf77, buf4, 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_8[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_9[grid(262144)](buf78,
            buf79, buf80, primals_28, primals_29, buf83, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_29
        buf84 = extern_kernels.convolution(buf83, primals_30, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf84, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf85 = buf84
        del buf84
        triton_poi_fused_add_13[grid(1048576)](buf85, buf66, 1048576,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf86 = buf80
        del buf80
        buf87 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf89 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_11[grid(128)](buf85, buf86,
            buf87, buf89, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf90 = empty_strided_cuda((4, 1024, 16, 16), (262144, 1, 16384, 
            1024), torch.float32)
        triton_poi_fused_native_group_norm_relu_12[grid(1048576)](buf85,
            buf86, buf87, primals_31, primals_32, buf90, 1048576, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_32
        buf91 = extern_kernels.convolution(buf90, primals_33, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf91, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf92 = buf87
        del buf87
        buf93 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf95 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_red_fused_native_group_norm_8[grid(128)](buf91, buf92, buf93,
            buf95, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        buf96 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_9[grid(262144)](buf91,
            buf92, buf93, primals_34, primals_35, buf96, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_35
        buf97 = extern_kernels.convolution(buf96, buf5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf97, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf98 = buf93
        del buf93
        buf99 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf101 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_8[grid(128)](buf97, buf98, buf99,
            buf101, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf102 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_9[grid(262144)](buf97,
            buf98, buf99, primals_37, primals_38, buf102, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_38
        buf103 = extern_kernels.convolution(buf102, primals_39, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf103, (4, 1024, 16, 16), (262144, 1, 16384, 1024))
        buf104 = buf103
        del buf103
        triton_poi_fused_add_13[grid(1048576)](buf104, buf85, 1048576,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf105 = buf99
        del buf99
        buf106 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf108 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_11[grid(128)](buf104, buf105,
            buf106, buf108, 128, 8192, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf109 = empty_strided_cuda((4, 1024, 16, 16), (262144, 1, 16384, 
            1024), torch.float32)
        triton_poi_fused_native_group_norm_relu_12[grid(1048576)](buf104,
            buf105, buf106, primals_40, primals_41, buf109, 1048576, XBLOCK
            =1024, num_warps=4, num_stages=1)
        del primals_41
        buf110 = extern_kernels.convolution(buf109, primals_42, stride=(2, 
            2), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf110, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf111 = extern_kernels.convolution(buf109, primals_43, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf111, (4, 512, 16, 16), (131072, 1, 8192, 512))
        buf112 = buf106
        del buf106
        buf113 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf115 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_14[grid(128)](buf111, buf112,
            buf113, buf115, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf116 = empty_strided_cuda((4, 512, 16, 16), (131072, 1, 8192, 512
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_15[grid(524288)](buf111,
            buf112, buf113, primals_44, primals_45, buf116, 524288, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_45
        buf117 = extern_kernels.convolution(buf116, buf6, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf117, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf118 = buf113
        del buf113
        buf119 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf121 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_16[grid(128)](buf117, buf118,
            buf119, buf121, 128, 1024, num_warps=8, num_stages=1)
        buf122 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_17[grid(131072)](buf117,
            buf118, buf119, primals_47, primals_48, buf122, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_48
        buf123 = extern_kernels.convolution(buf122, primals_49, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf123, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf124 = buf110
        del buf110
        triton_poi_fused_add_18[grid(524288)](buf124, buf123, 524288,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf125 = buf119
        del buf119
        buf126 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf128 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_19[grid(128)](buf124, buf125,
            buf126, buf128, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf129 = buf123
        del buf123
        triton_poi_fused_native_group_norm_relu_20[grid(524288)](buf124,
            buf125, buf126, primals_50, primals_51, buf129, 524288, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_51
        buf130 = extern_kernels.convolution(buf129, primals_52, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf130, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf131 = buf126
        del buf126
        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_per_fused_native_group_norm_16[grid(128)](buf130, buf131,
            buf132, buf134, 128, 1024, num_warps=8, num_stages=1)
        buf135 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_17[grid(131072)](buf130,
            buf131, buf132, primals_53, primals_54, buf135, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_54
        buf136 = extern_kernels.convolution(buf135, buf7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf136, (4, 512, 8, 8), (32768, 1, 4096, 512))
        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_per_fused_native_group_norm_16[grid(128)](buf136, buf137,
            buf138, buf140, 128, 1024, num_warps=8, num_stages=1)
        buf141 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_17[grid(131072)](buf136,
            buf137, buf138, primals_56, primals_57, buf141, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_57
        buf142 = extern_kernels.convolution(buf141, primals_58, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf142, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf143 = buf142
        del buf142
        triton_poi_fused_add_21[grid(524288)](buf143, buf124, 524288,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf144 = buf138
        del buf138
        buf145 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf147 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_19[grid(128)](buf143, buf144,
            buf145, buf147, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf148 = empty_strided_cuda((4, 2048, 8, 8), (131072, 1, 16384, 
            2048), torch.float32)
        triton_poi_fused_native_group_norm_relu_20[grid(524288)](buf143,
            buf144, buf145, primals_59, primals_60, buf148, 524288, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_60
        buf149 = extern_kernels.convolution(buf148, primals_61, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf149, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf150 = buf145
        del buf145
        buf151 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf153 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_16[grid(128)](buf149, buf150,
            buf151, buf153, 128, 1024, num_warps=8, num_stages=1)
        buf154 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_17[grid(131072)](buf149,
            buf150, buf151, primals_62, primals_63, buf154, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_63
        buf155 = extern_kernels.convolution(buf154, buf8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf155, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf156 = buf151
        del buf151
        buf157 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf159 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_16[grid(128)](buf155, buf156,
            buf157, buf159, 128, 1024, num_warps=8, num_stages=1)
        buf160 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_17[grid(131072)](buf155,
            buf156, buf157, primals_65, primals_66, buf160, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_66
        buf161 = extern_kernels.convolution(buf160, primals_67, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf161, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf162 = buf161
        del buf161
        triton_poi_fused_add_21[grid(524288)](buf162, buf143, 524288,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf163 = buf157
        del buf157
        buf164 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf166 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_19[grid(128)](buf162, buf163,
            buf164, buf166, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf167 = empty_strided_cuda((4, 2048, 8, 8), (131072, 1, 16384, 
            2048), torch.float32)
        triton_poi_fused_native_group_norm_relu_20[grid(524288)](buf162,
            buf163, buf164, primals_68, primals_69, buf167, 524288, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_69
        buf168 = extern_kernels.convolution(buf167, primals_70, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf168, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf169 = buf164
        del buf164
        buf170 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf172 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_16[grid(128)](buf168, buf169,
            buf170, buf172, 128, 1024, num_warps=8, num_stages=1)
        buf173 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_17[grid(131072)](buf168,
            buf169, buf170, primals_71, primals_72, buf173, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_72
        buf174 = extern_kernels.convolution(buf173, buf9, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf174, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf175 = buf170
        del buf170
        buf176 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf178 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_16[grid(128)](buf174, buf175,
            buf176, buf178, 128, 1024, num_warps=8, num_stages=1)
        buf179 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_17[grid(131072)](buf174,
            buf175, buf176, primals_74, primals_75, buf179, 131072, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_75
        buf180 = extern_kernels.convolution(buf179, primals_76, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf180, (4, 2048, 8, 8), (131072, 1, 16384, 2048))
        buf181 = buf180
        del buf180
        triton_poi_fused_add_21[grid(524288)](buf181, buf162, 524288,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf182 = buf176
        del buf176
        buf183 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf185 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_19[grid(128)](buf181, buf182,
            buf183, buf185, 128, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf186 = empty_strided_cuda((4, 2048, 8, 8), (131072, 1, 16384, 
            2048), torch.float32)
        triton_poi_fused_native_group_norm_relu_20[grid(524288)](buf181,
            buf182, buf183, primals_77, primals_78, buf186, 524288, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_78
        buf187 = extern_kernels.convolution(buf186, primals_79, stride=(2, 
            2), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf187, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf188 = extern_kernels.convolution(buf186, primals_80, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf188, (4, 1024, 8, 8), (65536, 1, 8192, 1024))
        buf189 = buf183
        del buf183
        buf190 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf192 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_22[grid(128)](buf188, buf189,
            buf190, buf192, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf193 = empty_strided_cuda((4, 1024, 8, 8), (65536, 1, 8192, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_23[grid(262144)](buf188,
            buf189, buf190, primals_81, primals_82, buf193, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_82
        buf194 = extern_kernels.convolution(buf193, buf10, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf194, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf195 = buf190
        del buf190
        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_24[grid(128)](buf194, buf195,
            buf196, buf198, 128, 512, num_warps=4, num_stages=1)
        buf199 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(65536)](buf194,
            buf195, buf196, primals_84, primals_85, buf199, 65536, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_85
        buf200 = extern_kernels.convolution(buf199, primals_86, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf200, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf201 = buf187
        del buf187
        triton_poi_fused_add_26[grid(262144)](buf201, buf200, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf202 = buf196
        del buf196
        buf203 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf205 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_27[grid(128)](buf201, buf202,
            buf203, buf205, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf206 = buf200
        del buf200
        triton_poi_fused_native_group_norm_relu_28[grid(262144)](buf201,
            buf202, buf203, primals_87, primals_88, buf206, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_88
        buf207 = extern_kernels.convolution(buf206, primals_89, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf207, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf208 = buf203
        del buf203
        buf209 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf211 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf207, buf208,
            buf209, buf211, 128, 512, num_warps=4, num_stages=1)
        buf212 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(65536)](buf207,
            buf208, buf209, primals_90, primals_91, buf212, 65536, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_91
        buf213 = extern_kernels.convolution(buf212, buf11, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf213, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf214 = buf209
        del buf209
        buf215 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf217 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf213, buf214,
            buf215, buf217, 128, 512, num_warps=4, num_stages=1)
        buf218 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(65536)](buf213,
            buf214, buf215, primals_93, primals_94, buf218, 65536, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_94
        buf219 = extern_kernels.convolution(buf218, primals_95, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf219, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf220 = buf219
        del buf219
        triton_poi_fused_add_29[grid(262144)](buf220, buf201, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf221 = buf215
        del buf215
        buf222 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf224 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_27[grid(128)](buf220, buf221,
            buf222, buf224, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf225 = empty_strided_cuda((4, 4096, 4, 4), (65536, 1, 16384, 4096
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_28[grid(262144)](buf220,
            buf221, buf222, primals_96, primals_97, buf225, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_97
        buf226 = extern_kernels.convolution(buf225, primals_98, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf226, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf227 = buf222
        del buf222
        buf228 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf230 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf226, buf227,
            buf228, buf230, 128, 512, num_warps=4, num_stages=1)
        buf231 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(65536)](buf226,
            buf227, buf228, primals_99, primals_100, buf231, 65536, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_100
        buf232 = extern_kernels.convolution(buf231, buf12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf232, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf233 = buf228
        del buf228
        buf234 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf236 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf232, buf233,
            buf234, buf236, 128, 512, num_warps=4, num_stages=1)
        buf237 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(65536)](buf232,
            buf233, buf234, primals_102, primals_103, buf237, 65536, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_103
        buf238 = extern_kernels.convolution(buf237, primals_104, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf238, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf239 = buf238
        del buf238
        triton_poi_fused_add_29[grid(262144)](buf239, buf220, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf240 = buf234
        del buf234
        buf241 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf243 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_red_fused_native_group_norm_27[grid(128)](buf239, buf240,
            buf241, buf243, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf244 = empty_strided_cuda((4, 4096, 4, 4), (65536, 1, 16384, 4096
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_28[grid(262144)](buf239,
            buf240, buf241, primals_105, primals_106, buf244, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_106
        buf245 = extern_kernels.convolution(buf244, primals_107, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf245, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf246 = buf241
        del buf241
        buf247 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf249 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf245, buf246,
            buf247, buf249, 128, 512, num_warps=4, num_stages=1)
        buf250 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(65536)](buf245,
            buf246, buf247, primals_108, primals_109, buf250, 65536, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_109
        buf251 = extern_kernels.convolution(buf250, buf13, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf251, (4, 1024, 4, 4), (16384, 1, 4096, 1024))
        buf252 = buf247
        del buf247
        buf253 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf255 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_24[grid(128)](buf251, buf252,
            buf253, buf255, 128, 512, num_warps=4, num_stages=1)
        buf256 = empty_strided_cuda((4, 1024, 4, 4), (16384, 1, 4096, 1024),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_25[grid(65536)](buf251,
            buf252, buf253, primals_111, primals_112, buf256, 65536, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_112
        buf257 = extern_kernels.convolution(buf256, primals_113, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf257, (4, 4096, 4, 4), (65536, 1, 16384, 4096))
        buf258 = buf257
        del buf257
        triton_poi_fused_add_29[grid(262144)](buf258, buf239, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf259 = buf253
        del buf253
        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_red_fused_native_group_norm_27[grid(128)](buf258, buf259,
            buf260, buf262, 128, 2048, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        buf263 = empty_strided_cuda((4, 4096, 4, 4), (65536, 1, 16384, 4096
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_28[grid(262144)](buf258,
            buf259, buf260, primals_114, primals_115, buf263, 262144,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_115
        buf264 = extern_kernels.convolution(buf263, primals_116, stride=(2,
            2), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf264, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf265 = extern_kernels.convolution(buf263, primals_117, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf265, (4, 2048, 4, 4), (32768, 1, 8192, 2048))
        buf266 = buf260
        del buf260
        buf267 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf269 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_30[grid(128)](buf265, buf266,
            buf267, buf269, 128, 1024, num_warps=8, num_stages=1)
        buf270 = empty_strided_cuda((4, 2048, 4, 4), (32768, 1, 8192, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_31[grid(131072)](buf265,
            buf266, buf267, primals_118, primals_119, buf270, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_119
        buf271 = extern_kernels.convolution(buf270, buf14, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf271, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf272 = buf267
        del buf267
        buf273 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf275 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_32[grid(128)](buf271, buf272,
            buf273, buf275, 128, 256, num_warps=2, num_stages=1)
        buf276 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_33[grid(32768)](buf271,
            buf272, buf273, primals_121, primals_122, buf276, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_122
        buf277 = extern_kernels.convolution(buf276, primals_123, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf277, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf278 = buf264
        del buf264
        triton_poi_fused_add_34[grid(131072)](buf278, buf277, 131072,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf279 = buf273
        del buf273
        buf280 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf282 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_35[grid(128)](buf278, buf279,
            buf280, buf282, 128, 1024, num_warps=8, num_stages=1)
        buf283 = buf277
        del buf277
        triton_poi_fused_native_group_norm_relu_36[grid(131072)](buf278,
            buf279, buf280, primals_124, primals_125, buf283, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_125
        buf284 = extern_kernels.convolution(buf283, primals_126, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf284, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf285 = buf280
        del buf280
        buf286 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf288 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_32[grid(128)](buf284, buf285,
            buf286, buf288, 128, 256, num_warps=2, num_stages=1)
        buf289 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_33[grid(32768)](buf284,
            buf285, buf286, primals_127, primals_128, buf289, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_128
        buf290 = extern_kernels.convolution(buf289, buf15, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf290, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf291 = buf286
        del buf286
        buf292 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf294 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_32[grid(128)](buf290, buf291,
            buf292, buf294, 128, 256, num_warps=2, num_stages=1)
        buf295 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_33[grid(32768)](buf290,
            buf291, buf292, primals_130, primals_131, buf295, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_131
        buf296 = extern_kernels.convolution(buf295, primals_132, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf296, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf297 = buf296
        del buf296
        triton_poi_fused_add_37[grid(131072)](buf297, buf278, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf298 = buf292
        del buf292
        buf299 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf301 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_35[grid(128)](buf297, buf298,
            buf299, buf301, 128, 1024, num_warps=8, num_stages=1)
        buf302 = empty_strided_cuda((4, 8192, 2, 2), (32768, 1, 16384, 8192
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_36[grid(131072)](buf297,
            buf298, buf299, primals_133, primals_134, buf302, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_134
        buf303 = extern_kernels.convolution(buf302, primals_135, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf303, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf304 = buf299
        del buf299
        buf305 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf307 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_32[grid(128)](buf303, buf304,
            buf305, buf307, 128, 256, num_warps=2, num_stages=1)
        buf308 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_33[grid(32768)](buf303,
            buf304, buf305, primals_136, primals_137, buf308, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_137
        buf309 = extern_kernels.convolution(buf308, buf16, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf309, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf310 = buf305
        del buf305
        buf311 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf313 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_32[grid(128)](buf309, buf310,
            buf311, buf313, 128, 256, num_warps=2, num_stages=1)
        buf314 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_33[grid(32768)](buf309,
            buf310, buf311, primals_139, primals_140, buf314, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_140
        buf315 = extern_kernels.convolution(buf314, primals_141, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf315, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf316 = buf315
        del buf315
        triton_poi_fused_add_37[grid(131072)](buf316, buf297, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf317 = buf311
        del buf311
        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_35[grid(128)](buf316, buf317,
            buf318, buf320, 128, 1024, num_warps=8, num_stages=1)
        buf321 = empty_strided_cuda((4, 8192, 2, 2), (32768, 1, 16384, 8192
            ), torch.float32)
        triton_poi_fused_native_group_norm_relu_36[grid(131072)](buf316,
            buf317, buf318, primals_142, primals_143, buf321, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_143
        buf322 = extern_kernels.convolution(buf321, primals_144, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf322, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        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_32[grid(128)](buf322, buf323,
            buf324, buf326, 128, 256, num_warps=2, num_stages=1)
        buf327 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_33[grid(32768)](buf322,
            buf323, buf324, primals_145, primals_146, buf327, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_146
        buf328 = extern_kernels.convolution(buf327, buf17, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf328, (4, 2048, 2, 2), (8192, 1, 4096, 2048))
        buf329 = buf324
        del buf324
        buf330 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf332 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        triton_per_fused_native_group_norm_32[grid(128)](buf328, buf329,
            buf330, buf332, 128, 256, num_warps=2, num_stages=1)
        buf333 = empty_strided_cuda((4, 2048, 2, 2), (8192, 1, 4096, 2048),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_33[grid(32768)](buf328,
            buf329, buf330, primals_148, primals_149, buf333, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_149
        buf334 = extern_kernels.convolution(buf333, primals_150, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf334, (4, 8192, 2, 2), (32768, 1, 16384, 8192))
        buf335 = buf334
        del buf334
        triton_poi_fused_add_37[grid(131072)](buf335, buf316, 131072,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf336 = reinterpret_tensor(buf330, (4, 32, 1, 1), (32, 1, 32, 32), 0)
        del buf330
        buf337 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch
            .float32)
        buf339 = reinterpret_tensor(buf337, (4, 32, 1, 1), (32, 1, 32, 32), 0)
        del buf337
        triton_per_fused_native_group_norm_38[grid(128)](buf339, buf335,
            buf336, 128, 1024, num_warps=8, num_stages=1)
        buf340 = empty_strided_cuda((4, 8192, 1, 1), (8192, 1, 8192, 8192),
            torch.float32)
        triton_poi_fused_mean_native_group_norm_relu_39[grid(32768)](buf335,
            buf336, buf339, primals_151, primals_152, buf340, 32768, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf341 = extern_kernels.convolution(buf340, primals_153, stride=(1,
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf341, (4, 21843, 1, 1), (21843, 1, 21843, 21843))
        buf342 = reinterpret_tensor(buf341, (4, 21843, 1, 1), (21843, 1, 
            87372, 87372), 0)
        del buf341
        triton_poi_fused_convolution_40[grid(87372)](buf342, primals_154, 
            87372, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_154
    return (reinterpret_tensor(buf342, (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, buf23, buf24, buf25,
        buf26, buf27, reinterpret_tensor(buf28, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf31, (4, 32), (32, 1), 0), buf32, buf34,
        reinterpret_tensor(buf35, (4, 32), (32, 1), 0), reinterpret_tensor(
        buf38, (4, 32), (32, 1), 0), buf39, buf40, reinterpret_tensor(buf41,
        (4, 32), (32, 1), 0), reinterpret_tensor(buf44, (4, 32), (32, 1), 0
        ), buf45, buf47, reinterpret_tensor(buf48, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf51, (4, 32), (32, 1), 0), buf52, buf53,
        reinterpret_tensor(buf54, (4, 32), (32, 1), 0), reinterpret_tensor(
        buf57, (4, 32), (32, 1), 0), buf58, buf59, reinterpret_tensor(buf60,
        (4, 32), (32, 1), 0), reinterpret_tensor(buf63, (4, 32), (32, 1), 0
        ), buf64, buf66, reinterpret_tensor(buf67, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf70, (4, 32), (32, 1), 0), 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, buf85, reinterpret_tensor(buf86, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf89, (4, 32), (32, 1), 0), buf90, buf91,
        reinterpret_tensor(buf92, (4, 32), (32, 1), 0), reinterpret_tensor(
        buf95, (4, 32), (32, 1), 0), buf96, buf97, reinterpret_tensor(buf98,
        (4, 32), (32, 1), 0), reinterpret_tensor(buf101, (4, 32), (32, 1), 
        0), buf102, buf104, reinterpret_tensor(buf105, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf108, (4, 32), (32, 1), 0), buf109, buf111,
        reinterpret_tensor(buf112, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf115, (4, 32), (32, 1), 0), buf116, buf117, reinterpret_tensor(
        buf118, (4, 32), (32, 1), 0), reinterpret_tensor(buf121, (4, 32), (
        32, 1), 0), buf122, buf124, reinterpret_tensor(buf125, (4, 32), (32,
        1), 0), reinterpret_tensor(buf128, (4, 32), (32, 1), 0), 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, buf143, reinterpret_tensor(
        buf144, (4, 32), (32, 1), 0), reinterpret_tensor(buf147, (4, 32), (
        32, 1), 0), buf148, buf149, reinterpret_tensor(buf150, (4, 32), (32,
        1), 0), reinterpret_tensor(buf153, (4, 32), (32, 1), 0), buf154,
        buf155, reinterpret_tensor(buf156, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf159, (4, 32), (32, 1), 0), buf160, buf162,
        reinterpret_tensor(buf163, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf166, (4, 32), (32, 1), 0), buf167, buf168, reinterpret_tensor(
        buf169, (4, 32), (32, 1), 0), reinterpret_tensor(buf172, (4, 32), (
        32, 1), 0), buf173, buf174, reinterpret_tensor(buf175, (4, 32), (32,
        1), 0), reinterpret_tensor(buf178, (4, 32), (32, 1), 0), buf179,
        buf181, reinterpret_tensor(buf182, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf185, (4, 32), (32, 1), 0), buf186, buf188,
        reinterpret_tensor(buf189, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf192, (4, 32), (32, 1), 0), buf193, buf194, reinterpret_tensor(
        buf195, (4, 32), (32, 1), 0), reinterpret_tensor(buf198, (4, 32), (
        32, 1), 0), buf199, buf201, reinterpret_tensor(buf202, (4, 32), (32,
        1), 0), reinterpret_tensor(buf205, (4, 32), (32, 1), 0), buf206,
        buf207, reinterpret_tensor(buf208, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf211, (4, 32), (32, 1), 0), buf212, buf213,
        reinterpret_tensor(buf214, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf217, (4, 32), (32, 1), 0), buf218, buf220, reinterpret_tensor(
        buf221, (4, 32), (32, 1), 0), reinterpret_tensor(buf224, (4, 32), (
        32, 1), 0), buf225, buf226, reinterpret_tensor(buf227, (4, 32), (32,
        1), 0), reinterpret_tensor(buf230, (4, 32), (32, 1), 0), buf231,
        buf232, reinterpret_tensor(buf233, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf236, (4, 32), (32, 1), 0), buf237, buf239,
        reinterpret_tensor(buf240, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf243, (4, 32), (32, 1), 0), buf244, buf245, reinterpret_tensor(
        buf246, (4, 32), (32, 1), 0), reinterpret_tensor(buf249, (4, 32), (
        32, 1), 0), buf250, buf251, reinterpret_tensor(buf252, (4, 32), (32,
        1), 0), reinterpret_tensor(buf255, (4, 32), (32, 1), 0), buf256,
        buf258, reinterpret_tensor(buf259, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf262, (4, 32), (32, 1), 0), buf263, buf265,
        reinterpret_tensor(buf266, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf269, (4, 32), (32, 1), 0), buf270, buf271, reinterpret_tensor(
        buf272, (4, 32), (32, 1), 0), reinterpret_tensor(buf275, (4, 32), (
        32, 1), 0), buf276, buf278, reinterpret_tensor(buf279, (4, 32), (32,
        1), 0), reinterpret_tensor(buf282, (4, 32), (32, 1), 0), buf283,
        buf284, reinterpret_tensor(buf285, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf288, (4, 32), (32, 1), 0), buf289, buf290,
        reinterpret_tensor(buf291, (4, 32), (32, 1), 0), reinterpret_tensor
        (buf294, (4, 32), (32, 1), 0), buf295, buf297, reinterpret_tensor(
        buf298, (4, 32), (32, 1), 0), reinterpret_tensor(buf301, (4, 32), (
        32, 1), 0), buf302, buf303, reinterpret_tensor(buf304, (4, 32), (32,
        1), 0), reinterpret_tensor(buf307, (4, 32), (32, 1), 0), buf308,
        buf309, reinterpret_tensor(buf310, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf313, (4, 32), (32, 1), 0), buf314, 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, buf328, reinterpret_tensor(buf329, (4, 32), (32,
        1), 0), reinterpret_tensor(buf332, (4, 32), (32, 1), 0), buf333,
        buf335, buf336, buf339, buf340)


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
        padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
        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
        s = w.std(dim=[1, 2, 3], keepdim=True)
        m = w.mean(dim=[1, 2, 3], keepdim=True)
        v = s * s
        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__()
        self.input_space = 'RGB'
        self.input_range = [0, 1]
        self.input_size = [3, 224, 224]
        self.mean = [0.5, 0.5, 0.5]
        self.std = [0.5, 0.5, 0.5]
        wf = width_factor
        self.root = nn.Sequential(OrderedDict([('conv', StdConv2d(3, 64 *
            wf, kernel_size=7, stride=2, padding=3, bias=False)), ('pool',
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1))]))
        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: 0.015035