# 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_7/inductor_cache/7n/c7nk2rbl67lakkbhjz3lqc5xbd3ao7y6jdx5wajcdriaj6jxs2po.py
# Topologically Sorted Source Nodes: [add, add_1, final_squeeze_tensor], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   final_squeeze_tensor => add_2
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_adaptive_avg_pool3d, 0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %_adaptive_avg_pool3d_1), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %_adaptive_avg_pool3d_2), kwargs = {})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_add_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x3 = (xindex // 64)
    x1 = (xindex // 4) % 4
    x6 = (xindex // 16)
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x1 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x6), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + (x7), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wn/cwn2swz25ji5y7ygs6wwihpkqqou43xvaxqan7bvon337mjfezlc.py
# Topologically Sorted Source Nodes: [conv3d, relu], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv3d => convolution
#   relu => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add_2, %primals_2, %primals_3, [1, 1, 1], [0, 0, 0], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_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.pointwise(
    size_hints=[512], 
    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_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 64) % 2
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/pl/cplxqsfqqof27kbjodzxu4pu6azywb4g5d5veo2vlgeqofc2mfzq.py
# Topologically Sorted Source Nodes: [conv3d_1, final_squeeze_tensor_1, output_tensor], Original ATen: [aten.convolution, aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   conv3d_1 => convolution_1
#   final_squeeze_tensor_1 => sigmoid
#   output_tensor => mul
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1, 1], [0, 0, 0], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %sigmoid), kwargs = {})
triton_poi_fused_convolution_mul_sigmoid_2 = async_compile.triton('triton_poi_fused_convolution_mul_sigmoid_2', '''
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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_mul_sigmoid_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_convolution_mul_sigmoid_2(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 64) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tl.sigmoid(tmp2)
    tmp5 = tmp3 * tmp4
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp5, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_3, (2, ), (1, ))
    assert_size_stride(primals_4, (4, 2, 1, 1, 1), (2, 1, 1, 1, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [squeeze_tensor_w], Original ATen: [aten._adaptive_avg_pool3d]
        buf0 = torch.ops.aten._adaptive_avg_pool3d.default(primals_1, [1, 1, 4])
        buf1 = buf0
        del buf0
        # Topologically Sorted Source Nodes: [squeeze_tensor_h], Original ATen: [aten._adaptive_avg_pool3d]
        buf2 = torch.ops.aten._adaptive_avg_pool3d.default(primals_1, [1, 4, 1])
        buf3 = buf2
        del buf2
        # Topologically Sorted Source Nodes: [squeeze_tensor_d], Original ATen: [aten._adaptive_avg_pool3d]
        buf4 = torch.ops.aten._adaptive_avg_pool3d.default(primals_1, [4, 1, 1])
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, add_1, final_squeeze_tensor], Original ATen: [aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_0.run(buf1, buf3, buf5, buf6, 1024, grid=grid(1024), stream=stream0)
        del buf1
        del buf3
        del buf5
        # Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, primals_2, 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, (4, 2, 4, 4, 4), (128, 64, 16, 4, 1))
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [conv3d, relu], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf8, primals_3, 512, grid=grid(512), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [conv3d_1], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_4, 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(buf9, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf10 = buf9; del buf9  # reuse
        buf11 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv3d_1, final_squeeze_tensor_1, output_tensor], Original ATen: [aten.convolution, aten.sigmoid, aten.mul]
        triton_poi_fused_convolution_mul_sigmoid_2.run(buf10, primals_5, primals_1, buf11, 1024, grid=grid(1024), stream=stream0)
        del primals_5
    return (buf11, primals_1, primals_2, primals_4, buf6, buf8, buf10, )


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((2, 4, 1, 1, 1), (4, 1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 2, 1, 1, 1), (2, 1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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
import torch.nn as nn
import torch.nn.functional as F


class ProjectExciteLayer(nn.Module):
    """
        Project & Excite Module, specifically designed for 3D inputs
        *quote*
    """

    def __init__(self, num_channels, reduction_ratio=2):
        """
        :param num_channels: No of input channels
        :param reduction_ratio: By how much should the num_channels should be reduced
        """
        super(ProjectExciteLayer, self).__init__()
        num_channels_reduced = num_channels // reduction_ratio
        self.reduction_ratio = reduction_ratio
        self.relu = nn.ReLU()
        self.conv_c = nn.Conv3d(in_channels=num_channels, out_channels=
            num_channels_reduced, kernel_size=1, stride=1)
        self.conv_cT = nn.Conv3d(in_channels=num_channels_reduced,
            out_channels=num_channels, kernel_size=1, stride=1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_tensor):
        """
        :param input_tensor: X, shape = (batch_size, num_channels, D, H, W)
        :return: output tensor
        """
        batch_size, num_channels, D, H, W = input_tensor.size()
        squeeze_tensor_w = F.adaptive_avg_pool3d(input_tensor, (1, 1, W))
        squeeze_tensor_h = F.adaptive_avg_pool3d(input_tensor, (1, H, 1))
        squeeze_tensor_d = F.adaptive_avg_pool3d(input_tensor, (D, 1, 1))
        final_squeeze_tensor = sum([squeeze_tensor_w.view(batch_size,
            num_channels, 1, 1, W), squeeze_tensor_h.view(batch_size,
            num_channels, 1, H, 1), squeeze_tensor_d.view(batch_size,
            num_channels, D, 1, 1)])
        final_squeeze_tensor = self.sigmoid(self.conv_cT(self.relu(self.
            conv_c(final_squeeze_tensor))))
        output_tensor = torch.mul(input_tensor, final_squeeze_tensor)
        return output_tensor


def get_inputs():
    return [torch.rand([4, 4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'num_channels': 4}]

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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x3 = xindex // 64
    x1 = xindex // 4 % 4
    x6 = xindex // 16
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr1 + (x1 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr2 + x6, xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + x7, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64 % 2
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_mul_sigmoid_2(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tl.sigmoid(tmp2)
    tmp5 = tmp3 * tmp4
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_3, (2,), (1,))
    assert_size_stride(primals_4, (4, 2, 1, 1, 1), (2, 1, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten._adaptive_avg_pool3d.default(primals_1, [1, 1, 4]
            )
        buf1 = buf0
        del buf0
        buf2 = torch.ops.aten._adaptive_avg_pool3d.default(primals_1, [1, 4, 1]
            )
        buf3 = buf2
        del buf2
        buf4 = torch.ops.aten._adaptive_avg_pool3d.default(primals_1, [4, 1, 1]
            )
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(1024)](buf1, buf3, buf5, buf6, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del buf3
        del buf5
        buf7 = extern_kernels.convolution(buf6, primals_2, 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, (4, 2, 4, 4, 4), (128, 64, 16, 4, 1))
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_relu_1[grid(512)](buf8, primals_3, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf9 = extern_kernels.convolution(buf8, primals_4, 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(buf9, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf10 = buf9
        del buf9
        buf11 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_convolution_mul_sigmoid_2[grid(1024)](buf10,
            primals_5, primals_1, buf11, 1024, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_5
    return buf11, primals_1, primals_2, primals_4, buf6, buf8, buf10


class ProjectExciteLayerNew(nn.Module):
    """
        Project & Excite Module, specifically designed for 3D inputs
        *quote*
    """

    def __init__(self, num_channels, reduction_ratio=2):
        """
        :param num_channels: No of input channels
        :param reduction_ratio: By how much should the num_channels should be reduced
        """
        super(ProjectExciteLayerNew, self).__init__()
        num_channels_reduced = num_channels // reduction_ratio
        self.reduction_ratio = reduction_ratio
        self.relu = nn.ReLU()
        self.conv_c = nn.Conv3d(in_channels=num_channels, out_channels=
            num_channels_reduced, kernel_size=1, stride=1)
        self.conv_cT = nn.Conv3d(in_channels=num_channels_reduced,
            out_channels=num_channels, kernel_size=1, stride=1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_0):
        primals_2 = self.conv_c.weight
        primals_3 = self.conv_c.bias
        primals_4 = self.conv_cT.weight
        primals_5 = self.conv_cT.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
