# 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_4/inductor_cache/ol/col24ddvbqyefqt5rfz2zjvznr2qnowz53pao4cjyjumxfxxw3h4.py
# Topologically Sorted Source Nodes: [enc], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   enc => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%getitem, %avg_pool3d, %convolution], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 3
    x0 = xindex % 16
    x2 = (xindex // 48)
    x3 = xindex
    tmp25 = tl.load(in_ptr3 + (0))
    tmp26 = tl.broadcast_to(tmp25, [XBLOCK])
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (64*x2) + (64*((-1) + x1))), tmp9 & xmask, other=0.0)
    tmp11 = tl.load(in_ptr1 + (16 + x0 + (64*x2) + (64*((-1) + x1))), tmp9 & xmask, other=0.0)
    tmp12 = tmp11 + tmp10
    tmp13 = tl.load(in_ptr1 + (32 + x0 + (64*x2) + (64*((-1) + x1))), tmp9 & xmask, other=0.0)
    tmp14 = tmp13 + tmp12
    tmp15 = tl.load(in_ptr1 + (48 + x0 + (64*x2) + (64*((-1) + x1))), tmp9 & xmask, other=0.0)
    tmp16 = tmp15 + tmp14
    tmp17 = 0.25
    tmp18 = tmp16 * tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp9, tmp18, tmp19)
    tmp21 = tmp0 >= tmp7
    tmp22 = tl.full([1], 3, tl.int64)
    tmp23 = tmp0 < tmp22
    tmp24 = tl.load(in_ptr2 + (x0 + (16*x2)), tmp21 & xmask, eviction_policy='evict_last', other=0.0)
    tmp27 = tmp24 + tmp26
    tmp28 = tl.full(tmp27.shape, 0.0, tmp27.dtype)
    tmp29 = tl.where(tmp21, tmp27, tmp28)
    tmp30 = tl.where(tmp9, tmp20, tmp29)
    tmp31 = tl.where(tmp4, tmp5, tmp30)
    tl.store(out_ptr0 + (x3), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/uv/cuvmuwhltr4vdkrv4aeidlasov6zpjxspaoakfc6bklsgflly3f4.py
# Topologically Sorted Source Nodes: [enc_1, dec_1, add, out], Original ATen: [aten.convolution, aten.add, aten.sigmoid]
# Source node to ATen node mapping:
#   add => add
#   dec_1 => convolution_3
#   enc_1 => convolution_1
#   out => sigmoid
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_4, %primals_5, [1, 1], [3, 3], [1, 1], False, [0, 0], 1), kwargs = {})
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_1, %primals_9, %primals_10, [1, 1], [3, 3], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, %convolution_3), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add,), kwargs = {})
triton_poi_fused_add_convolution_sigmoid_1 = async_compile.triton('triton_poi_fused_add_convolution_sigmoid_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=[64], 
    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_convolution_sigmoid_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_convolution_sigmoid_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp5 = tl.load(in_ptr2 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp7 = tmp4 + tmp6
    tmp8 = tmp3 + tmp7
    tmp9 = tl.sigmoid(tmp8)
    tl.store(in_out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (1, 3, 7, 7), (147, 49, 7, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_8, (1, ), (1, ))
    assert_size_stride(primals_9, (1, 3, 7, 7), (147, 49, 7, 1))
    assert_size_stride(primals_10, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [max_pool3d], Original ATen: [aten.max_pool3d_with_indices]
        buf0 = torch.ops.aten.max_pool3d_with_indices.default(primals_1, [4, 1, 1], [4, 1, 1])
        buf1 = buf0[0]
        del buf0
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [enc], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(buf1, primals_1, buf3, primals_3, buf4, 192, grid=grid(192), stream=stream0)
        del buf1
        del buf3
        del primals_3
        # Topologically Sorted Source Nodes: [enc_1], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1, 1), padding=(3, 3), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 1, 4, 4), (16, 16, 4, 1))
        # Topologically Sorted Source Nodes: [max_pool3d_1], Original ATen: [aten.max_pool3d_with_indices]
        buf6 = torch.ops.aten.max_pool3d_with_indices.default(primals_6, [4, 1, 1], [4, 1, 1])
        buf7 = buf6[0]
        del buf6
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(primals_6, primals_7, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 1, 4, 4), (16, 16, 4, 1))
        buf10 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [dec], Original ATen: [aten.cat]
        triton_poi_fused_cat_0.run(buf7, primals_6, buf9, primals_8, buf10, 192, grid=grid(192), stream=stream0)
        del buf7
        del buf9
        del primals_8
        # Topologically Sorted Source Nodes: [dec_1], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf10, primals_9, stride=(1, 1), padding=(3, 3), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 1, 4, 4), (16, 16, 4, 1))
        buf12 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [enc_1, dec_1, add, out], Original ATen: [aten.convolution, aten.add, aten.sigmoid]
        triton_poi_fused_add_convolution_sigmoid_1.run(buf12, buf5, primals_5, primals_10, 64, grid=grid(64), stream=stream0)
        del buf5
        del primals_10
        del primals_5
    return (buf12, primals_1, primals_2, primals_4, primals_6, primals_7, primals_9, buf4, buf10, buf12, )


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), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 3, 7, 7), (147, 49, 7, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, 3, 7, 7), (147, 49, 7, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
    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


class sAG(nn.Module):

    def __init__(self, num_channels_in_enc, num_channels_in_dec):
        super(sAG, self).__init__()
        self.num_channels_in_enc = num_channels_in_enc
        self.num_channels_in_dec = num_channels_in_dec
        self.ch_max_pool_enc = nn.MaxPool3d(kernel_size=(self.
            num_channels_in_enc, 1, 1))
        self.ch_avg_pool_enc = nn.AvgPool3d(kernel_size=(self.
            num_channels_in_enc, 1, 1))
        self.conv1_enc = nn.Conv2d(in_channels=self.num_channels_in_enc,
            out_channels=1, kernel_size=1, stride=1, padding=0)
        self.conv2_enc = nn.Conv2d(in_channels=3, out_channels=1,
            kernel_size=7, stride=1, padding=3)
        self.ch_max_pool_dec = nn.MaxPool3d(kernel_size=(self.
            num_channels_in_dec, 1, 1))
        self.ch_avg_pool_dec = nn.AvgPool3d(kernel_size=(self.
            num_channels_in_dec, 1, 1))
        self.conv1_dec = nn.Conv2d(in_channels=self.num_channels_in_dec,
            out_channels=1, kernel_size=1, stride=1, padding=0)
        self.conv2_dec = nn.Conv2d(in_channels=3, out_channels=1,
            kernel_size=7, stride=1, padding=3)

    def forward(self, enc, dec):
        enc = torch.cat(tensors=(self.ch_max_pool_enc(enc), self.
            ch_avg_pool_enc(enc), self.conv1_enc(enc)), dim=1)
        enc = self.conv2_enc(enc)
        dec = torch.cat(tensors=(self.ch_max_pool_dec(dec), self.
            ch_avg_pool_dec(dec), self.conv1_dec(dec)), dim=1)
        dec = self.conv2_dec(dec)
        out = torch.sigmoid(enc + dec)
        return out

    def initialize_weights(self):
        for layer in [self.conv1_enc, self.conv1_dec, self.conv2_enc, self.
            conv2_dec]:
            nn.init.normal_(layer.weight.data, mean=0.0, std=0.02)
            nn.init.constant_(layer.bias.data, 0.0)


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


def get_init_inputs():
    return [[], {'num_channels_in_enc': 4, 'num_channels_in_dec': 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
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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 3
    x0 = xindex % 16
    x2 = xindex // 48
    x3 = xindex
    tmp25 = tl.load(in_ptr3 + 0)
    tmp26 = tl.broadcast_to(tmp25, [XBLOCK])
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 64 * x2 + 64 * (-1 + x1)), tmp9 & xmask,
        other=0.0)
    tmp11 = tl.load(in_ptr1 + (16 + x0 + 64 * x2 + 64 * (-1 + x1)), tmp9 &
        xmask, other=0.0)
    tmp12 = tmp11 + tmp10
    tmp13 = tl.load(in_ptr1 + (32 + x0 + 64 * x2 + 64 * (-1 + x1)), tmp9 &
        xmask, other=0.0)
    tmp14 = tmp13 + tmp12
    tmp15 = tl.load(in_ptr1 + (48 + x0 + 64 * x2 + 64 * (-1 + x1)), tmp9 &
        xmask, other=0.0)
    tmp16 = tmp15 + tmp14
    tmp17 = 0.25
    tmp18 = tmp16 * tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp9, tmp18, tmp19)
    tmp21 = tmp0 >= tmp7
    tl.full([1], 3, tl.int64)
    tmp24 = tl.load(in_ptr2 + (x0 + 16 * x2), tmp21 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp27 = tmp24 + tmp26
    tmp28 = tl.full(tmp27.shape, 0.0, tmp27.dtype)
    tmp29 = tl.where(tmp21, tmp27, tmp28)
    tmp30 = tl.where(tmp9, tmp20, tmp29)
    tmp31 = tl.where(tmp4, tmp5, tmp30)
    tl.store(out_ptr0 + x3, tmp31, xmask)


@triton.jit
def triton_poi_fused_add_convolution_sigmoid_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_out_ptr0 + x0, xmask)
    tmp5 = tl.load(in_ptr2 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp7 = tmp4 + tmp6
    tmp8 = tmp3 + tmp7
    tmp9 = tl.sigmoid(tmp8)
    tl.store(in_out_ptr0 + x0, tmp9, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (1, 3, 7, 7), (147, 49, 7, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_8, (1,), (1,))
    assert_size_stride(primals_9, (1, 3, 7, 7), (147, 49, 7, 1))
    assert_size_stride(primals_10, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten.max_pool3d_with_indices.default(primals_1, [4,
            1, 1], [4, 1, 1])
        buf1 = buf0[0]
        del buf0
        buf3 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(192)](buf1, primals_1, buf3, primals_3,
            buf4, 192, XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del buf3
        del primals_3
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1, 1),
            padding=(3, 3), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 1, 4, 4), (16, 16, 4, 1))
        buf6 = torch.ops.aten.max_pool3d_with_indices.default(primals_6, [4,
            1, 1], [4, 1, 1])
        buf7 = buf6[0]
        del buf6
        buf9 = extern_kernels.convolution(primals_6, primals_7, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 1, 4, 4), (16, 16, 4, 1))
        buf10 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        triton_poi_fused_cat_0[grid(192)](buf7, primals_6, buf9, primals_8,
            buf10, 192, XBLOCK=256, num_warps=4, num_stages=1)
        del buf7
        del buf9
        del primals_8
        buf11 = extern_kernels.convolution(buf10, primals_9, stride=(1, 1),
            padding=(3, 3), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 1, 4, 4), (16, 16, 4, 1))
        buf12 = buf11
        del buf11
        triton_poi_fused_add_convolution_sigmoid_1[grid(64)](buf12, buf5,
            primals_5, primals_10, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf5
        del primals_10
        del primals_5
    return (buf12, primals_1, primals_2, primals_4, primals_6, primals_7,
        primals_9, buf4, buf10, buf12)


class sAGNew(nn.Module):

    def __init__(self, num_channels_in_enc, num_channels_in_dec):
        super(sAGNew, self).__init__()
        self.num_channels_in_enc = num_channels_in_enc
        self.num_channels_in_dec = num_channels_in_dec
        self.ch_max_pool_enc = nn.MaxPool3d(kernel_size=(self.
            num_channels_in_enc, 1, 1))
        self.ch_avg_pool_enc = nn.AvgPool3d(kernel_size=(self.
            num_channels_in_enc, 1, 1))
        self.conv1_enc = nn.Conv2d(in_channels=self.num_channels_in_enc,
            out_channels=1, kernel_size=1, stride=1, padding=0)
        self.conv2_enc = nn.Conv2d(in_channels=3, out_channels=1,
            kernel_size=7, stride=1, padding=3)
        self.ch_max_pool_dec = nn.MaxPool3d(kernel_size=(self.
            num_channels_in_dec, 1, 1))
        self.ch_avg_pool_dec = nn.AvgPool3d(kernel_size=(self.
            num_channels_in_dec, 1, 1))
        self.conv1_dec = nn.Conv2d(in_channels=self.num_channels_in_dec,
            out_channels=1, kernel_size=1, stride=1, padding=0)
        self.conv2_dec = nn.Conv2d(in_channels=3, out_channels=1,
            kernel_size=7, stride=1, padding=3)

    def initialize_weights(self):
        for layer in [self.conv1_enc, self.conv1_dec, self.conv2_enc, self.
            conv2_dec]:
            nn.init.normal_(layer.weight.data, mean=0.0, std=0.02)
            nn.init.constant_(layer.bias.data, 0.0)

    def forward(self, input_0, input_1):
        primals_2 = self.conv1_enc.weight
        primals_3 = self.conv1_enc.bias
        primals_4 = self.conv2_enc.weight
        primals_5 = self.conv2_enc.bias
        primals_7 = self.conv1_dec.weight
        primals_8 = self.conv1_dec.bias
        primals_9 = self.conv2_dec.weight
        primals_10 = self.conv2_dec.bias
        primals_1 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]
