# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_8/inductor_cache/la/clavtu6hpoe4jhvnfgb4ljkvhejbmjerj6vioxexb472pu65xrjq.py
# Topologically Sorted Source Nodes: [mean, mean_1], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   mean => mean
#   mean_1 => mean_1
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [1], True), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mean, [2], True), kwargs = {})
triton_poi_fused_mean_0 = async_compile.triton('triton_poi_fused_mean_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mean_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (256*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0 + (256*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0 + (256*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x0 + (256*x1)), xmask)
    tmp9 = tl.load(in_ptr0 + (16 + x0 + (256*x1)), xmask)
    tmp10 = tl.load(in_ptr0 + (80 + x0 + (256*x1)), xmask)
    tmp12 = tl.load(in_ptr0 + (144 + x0 + (256*x1)), xmask)
    tmp14 = tl.load(in_ptr0 + (208 + x0 + (256*x1)), xmask)
    tmp18 = tl.load(in_ptr0 + (32 + x0 + (256*x1)), xmask)
    tmp19 = tl.load(in_ptr0 + (96 + x0 + (256*x1)), xmask)
    tmp21 = tl.load(in_ptr0 + (160 + x0 + (256*x1)), xmask)
    tmp23 = tl.load(in_ptr0 + (224 + x0 + (256*x1)), xmask)
    tmp27 = tl.load(in_ptr0 + (48 + x0 + (256*x1)), xmask)
    tmp28 = tl.load(in_ptr0 + (112 + x0 + (256*x1)), xmask)
    tmp30 = tl.load(in_ptr0 + (176 + x0 + (256*x1)), xmask)
    tmp32 = tl.load(in_ptr0 + (240 + x0 + (256*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = tmp8 + tmp16
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = tmp17 + tmp25
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = tmp26 + tmp34
    tmp36 = tmp35 / tmp7
    tl.store(out_ptr0 + (x2), tmp36, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/tq/ctqeugntuzvh57i2whcv3mhfjtlgw6jkurzi5t3exstjcx4pyloq.py
# Topologically Sorted Source Nodes: [mean_2, weight_mean, weight, var, add, sqrt, weight_1], Original ATen: [aten.mean, aten.sub, aten.var, aten.add, aten.sqrt, aten.div]
# Source node to ATen node mapping:
#   add => add
#   mean_2 => mean_2
#   sqrt => sqrt
#   var => var
#   weight => sub
#   weight_1 => div
#   weight_mean => mean_3
# Graph fragment:
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mean_1, [3], True), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mean_2, [4], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %mean_3), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view, [1]), kwargs = {correction: 1})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%var, 1e-12), kwargs = {})
#   %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %expand), kwargs = {})
triton_per_fused_add_div_mean_sqrt_sub_var_1 = async_compile.triton('triton_per_fused_add_div_mean_sqrt_sub_var_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[4, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_sqrt_sub_var_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 17, 'num_reduction': 3, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_div_mean_sqrt_sub_var_1(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 4
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    x0 = xindex
    r1 = rindex
    tmp0 = tl.load(in_ptr0 + (16*x0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (4 + (16*x0)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (8 + (16*x0)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (12 + (16*x0)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + (16*x0)), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (5 + (16*x0)), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (9 + (16*x0)), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (13 + (16*x0)), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (2 + (16*x0)), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (6 + (16*x0)), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (10 + (16*x0)), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (14 + (16*x0)), None, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (3 + (16*x0)), None, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (7 + (16*x0)), None, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (11 + (16*x0)), None, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr0 + (15 + (16*x0)), None, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr1 + (r1 + (256*x0)), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = tmp8 + tmp16
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = tmp17 + tmp25
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = tmp26 + tmp34
    tmp36 = tmp35 / tmp7
    tmp38 = tmp37 - tmp36
    tmp39 = tl.broadcast_to(tmp38, [RBLOCK])
    tmp41 = tl.broadcast_to(tmp39, [RBLOCK])
    tmp43 = triton_helpers.promote_to_tensor(tl.sum(tmp41, 0))
    tmp44 = tl.full([1], 256, tl.int32)
    tmp45 = tmp44.to(tl.float32)
    tmp46 = tmp43 / tmp45
    tmp47 = tmp39 - tmp46
    tmp48 = tmp47 * tmp47
    tmp49 = tl.broadcast_to(tmp48, [RBLOCK])
    tmp51 = triton_helpers.promote_to_tensor(tl.sum(tmp49, 0))
    tmp52 = 255.0
    tmp53 = tmp51 / tmp52
    tmp54 = 1e-12
    tmp55 = tmp53 + tmp54
    tmp56 = libdevice.sqrt(tmp55)
    tmp57 = tmp38 / tmp56
    tl.store(out_ptr0 + (x0), tmp36, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp56, None)
    tl.store(out_ptr1 + (r1 + (256*x0)), tmp57, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 4, 4), (16, 64, 64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean, mean_1], Original ATen: [aten.mean]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mean_0.run(primals_1, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((4, 1, 1, 1, 1), (1, 4, 4, 4, 4), torch.float32)
        buf3 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf5 = buf3; del buf3  # reuse
        buf6 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean_2, weight_mean, weight, var, add, sqrt, weight_1], Original ATen: [aten.mean, aten.sub, aten.var, aten.add, aten.sqrt, aten.div]
        triton_per_fused_add_div_mean_sqrt_sub_var_1.run(buf5, buf0, primals_1, buf1, buf6, 4, 256, grid=grid(4), stream=stream0)
        del buf0
        del buf1
        # Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(reinterpret_tensor(primals_2, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), buf6, stride=(1, 1, 1), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (1, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    return (reinterpret_tensor(buf7, (4, 1, 1, 1), (1, 1, 1, 1), 0), primals_1, buf5, buf6, reinterpret_tensor(primals_2, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

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
from torch import nn
import torch.jit
import torch.nn.functional
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_mean_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0 + 256 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0 + 256 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x0 + 256 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (16 + x0 + 256 * x1), xmask)
    tmp10 = tl.load(in_ptr0 + (80 + x0 + 256 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (144 + x0 + 256 * x1), xmask)
    tmp14 = tl.load(in_ptr0 + (208 + x0 + 256 * x1), xmask)
    tmp18 = tl.load(in_ptr0 + (32 + x0 + 256 * x1), xmask)
    tmp19 = tl.load(in_ptr0 + (96 + x0 + 256 * x1), xmask)
    tmp21 = tl.load(in_ptr0 + (160 + x0 + 256 * x1), xmask)
    tmp23 = tl.load(in_ptr0 + (224 + x0 + 256 * x1), xmask)
    tmp27 = tl.load(in_ptr0 + (48 + x0 + 256 * x1), xmask)
    tmp28 = tl.load(in_ptr0 + (112 + x0 + 256 * x1), xmask)
    tmp30 = tl.load(in_ptr0 + (176 + x0 + 256 * x1), xmask)
    tmp32 = tl.load(in_ptr0 + (240 + x0 + 256 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = tmp8 + tmp16
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = tmp17 + tmp25
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = tmp26 + tmp34
    tmp36 = tmp35 / tmp7
    tl.store(out_ptr0 + x2, tmp36, xmask)


@triton.jit
def triton_per_fused_add_div_mean_sqrt_sub_var_1(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    x0 = xindex
    r1 = rindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (4 + 16 * x0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (8 + 16 * x0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (12 + 16 * x0), None, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (1 + 16 * x0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (5 + 16 * x0), None, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr0 + (9 + 16 * x0), None, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr0 + (13 + 16 * x0), None, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr0 + (2 + 16 * x0), None, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr0 + (6 + 16 * x0), None, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr0 + (10 + 16 * x0), None, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (14 + 16 * x0), None, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (3 + 16 * x0), None, eviction_policy='evict_last'
        )
    tmp28 = tl.load(in_ptr0 + (7 + 16 * x0), None, eviction_policy='evict_last'
        )
    tmp30 = tl.load(in_ptr0 + (11 + 16 * x0), None, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr0 + (15 + 16 * x0), None, eviction_policy=
        'evict_last')
    tmp37 = tl.load(in_ptr1 + (r1 + 256 * x0), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = tmp8 + tmp16
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = tmp17 + tmp25
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = tmp26 + tmp34
    tmp36 = tmp35 / tmp7
    tmp38 = tmp37 - tmp36
    tmp39 = tl.broadcast_to(tmp38, [RBLOCK])
    tmp41 = tl.broadcast_to(tmp39, [RBLOCK])
    tmp43 = triton_helpers.promote_to_tensor(tl.sum(tmp41, 0))
    tmp44 = tl.full([1], 256, tl.int32)
    tmp45 = tmp44.to(tl.float32)
    tmp46 = tmp43 / tmp45
    tmp47 = tmp39 - tmp46
    tmp48 = tmp47 * tmp47
    tmp49 = tl.broadcast_to(tmp48, [RBLOCK])
    tmp51 = triton_helpers.promote_to_tensor(tl.sum(tmp49, 0))
    tmp52 = 255.0
    tmp53 = tmp51 / tmp52
    tmp54 = 1e-12
    tmp55 = tmp53 + tmp54
    tmp56 = libdevice.sqrt(tmp55)
    tmp57 = tmp38 / tmp56
    tl.store(out_ptr0 + x0, tmp36, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp56, None)
    tl.store(out_ptr1 + (r1 + 256 * x0), tmp57, None)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 4, 4), (16, 64, 64, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 1, 1, 1, 1), (1, 4, 4, 4, 4), torch.
            float32)
        buf3 = empty_strided_cuda((4,), (1,), torch.float32)
        buf5 = buf3
        del buf3
        buf6 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_per_fused_add_div_mean_sqrt_sub_var_1[grid(4)](buf5, buf0,
            primals_1, buf1, buf6, 4, 256, num_warps=2, num_stages=1)
        del buf0
        del buf1
        buf7 = extern_kernels.convolution(reinterpret_tensor(primals_2, (1,
            4, 4, 4, 4), (256, 64, 16, 4, 1), 0), buf6, stride=(1, 1, 1),
            padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (1, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    return reinterpret_tensor(buf7, (4, 1, 1, 1), (1, 1, 1, 1), 0
        ), primals_1, buf5, buf6, reinterpret_tensor(primals_2, (1, 4, 4, 4,
        4), (256, 64, 16, 4, 1), 0)


class Conv3dNew(nn.Conv3d):

    def __init__(self, in_channels, out_channels, kernel_size, stride=(1, 1,
        1), padding=(0, 0, 0), dilation=(1, 1, 1), groups=1, bias=False):
        super(Conv3dNew, self).__init__(in_channels, out_channels,
            kernel_size, stride, padding, dilation, groups, bias)

    def forward(self, input_0):
        primals_1 = self.weight
        primals_2 = input_0
        output = call([primals_1, primals_2])
        return output[0]
