# AOT ID: ['0_inference']
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/4b/c4bgdlpypj3woeewvt2i3rksrpsq5fwizgew7fc2qg4hekpjtaic.py
# Topologically Sorted Source Nodes: [prob_map, sum_1, sum_2], Original ATen: [aten.sigmoid, aten.sum]
# Source node to ATen node mapping:
#   prob_map => sigmoid
#   sum_1 => sum_1
#   sum_2 => sum_2
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sigmoid, [2], True), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_1, [3], True), kwargs = {})
triton_poi_fused_sigmoid_sum_0 = async_compile.triton('triton_poi_fused_sigmoid_sum_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=[16], 
    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_sigmoid_sum_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_sigmoid_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (16*x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp43 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = tmp1 + tmp3
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = tmp4 + tmp6
    tmp9 = tl.sigmoid(tmp8)
    tmp10 = tmp7 + tmp9
    tmp12 = tl.sigmoid(tmp11)
    tmp14 = tl.sigmoid(tmp13)
    tmp15 = tmp12 + tmp14
    tmp17 = tl.sigmoid(tmp16)
    tmp18 = tmp15 + tmp17
    tmp20 = tl.sigmoid(tmp19)
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 + tmp21
    tmp24 = tl.sigmoid(tmp23)
    tmp26 = tl.sigmoid(tmp25)
    tmp27 = tmp24 + tmp26
    tmp29 = tl.sigmoid(tmp28)
    tmp30 = tmp27 + tmp29
    tmp32 = tl.sigmoid(tmp31)
    tmp33 = tmp30 + tmp32
    tmp34 = tmp22 + tmp33
    tmp36 = tl.sigmoid(tmp35)
    tmp38 = tl.sigmoid(tmp37)
    tmp39 = tmp36 + tmp38
    tmp41 = tl.sigmoid(tmp40)
    tmp42 = tmp39 + tmp41
    tmp44 = tl.sigmoid(tmp43)
    tmp45 = tmp42 + tmp44
    tmp46 = tmp34 + tmp45
    tl.store(out_ptr0 + (x0), tmp46, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wa/cwa4zte2kbak7nfbarayipc3unmldhhf2ercj6hghohcfm22vxs6.py
# Topologically Sorted Source Nodes: [prob_map, weight_map, mul, sum_3], Original ATen: [aten.sigmoid, aten.div, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   mul => mul
#   prob_map => sigmoid
#   sum_3 => sum_3
#   weight_map => div
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sigmoid, %sum_2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %div), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2], True), kwargs = {})
triton_poi_fused_div_mul_sigmoid_sum_1 = async_compile.triton('triton_poi_fused_div_mul_sigmoid_sum_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_div_mul_sigmoid_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_div_mul_sigmoid_sum_1(in_ptr0, in_ptr1, in_ptr2, 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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (16*x1)), xmask)
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0 + (16*x1)), xmask)
    tmp12 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp13 = tl.load(in_ptr1 + (8 + x0 + (16*x1)), xmask)
    tmp18 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp19 = tl.load(in_ptr1 + (12 + x0 + (16*x1)), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp4 = tmp2 / tmp3
    tmp5 = tmp0 * tmp4
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tmp8 / tmp3
    tmp10 = tmp6 * tmp9
    tmp11 = tmp5 + tmp10
    tmp14 = tl.sigmoid(tmp13)
    tmp15 = tmp14 / tmp3
    tmp16 = tmp12 * tmp15
    tmp17 = tmp11 + tmp16
    tmp20 = tl.sigmoid(tmp19)
    tmp21 = tmp20 / tmp3
    tmp22 = tmp18 * tmp21
    tmp23 = tmp17 + tmp22
    tl.store(out_ptr0 + (x2), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/22/c22ds4dhddfi2vv7kw3j6vphvcnvlghhb2l5dprws7csq7qma25k.py
# Topologically Sorted Source Nodes: [feat], Original ATen: [aten.sum]
# Source node to ATen node mapping:
#   feat => sum_4
# Graph fragment:
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_3, [3], True), kwargs = {})
triton_poi_fused_sum_2 = async_compile.triton('triton_poi_fused_sum_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=[16], 
    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_sum_2', 'mutated_arg_names': [], '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_sum_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [prob_map, sum_1, sum_2], Original ATen: [aten.sigmoid, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_sigmoid_sum_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [prob_map, weight_map, mul, sum_3], Original ATen: [aten.sigmoid, aten.div, aten.mul, aten.sum]
        triton_poi_fused_div_mul_sigmoid_sum_1.run(arg1_1, arg0_1, buf0, buf1, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        buf2 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [feat], Original ATen: [aten.sum]
        triton_poi_fused_sum_2.run(buf1, buf2, 16, grid=grid(16), stream=stream0)
        del buf1
    return (buf2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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 PcamPool(nn.Module):

    def __init__(self):
        super(PcamPool, self).__init__()

    def forward(self, feat_map, logit_map):
        assert logit_map is not None
        prob_map = torch.sigmoid(logit_map)
        weight_map = prob_map / prob_map.sum(dim=2, keepdim=True).sum(dim=3,
            keepdim=True)
        feat = (feat_map * weight_map).sum(dim=2, keepdim=True).sum(dim=3,
            keepdim=True)
        return feat


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


def get_init_inputs():
    return [[], {}]

import torch
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_sigmoid_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp31 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp35 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp37 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp40 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp43 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = tmp1 + tmp3
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = tmp4 + tmp6
    tmp9 = tl.sigmoid(tmp8)
    tmp10 = tmp7 + tmp9
    tmp12 = tl.sigmoid(tmp11)
    tmp14 = tl.sigmoid(tmp13)
    tmp15 = tmp12 + tmp14
    tmp17 = tl.sigmoid(tmp16)
    tmp18 = tmp15 + tmp17
    tmp20 = tl.sigmoid(tmp19)
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 + tmp21
    tmp24 = tl.sigmoid(tmp23)
    tmp26 = tl.sigmoid(tmp25)
    tmp27 = tmp24 + tmp26
    tmp29 = tl.sigmoid(tmp28)
    tmp30 = tmp27 + tmp29
    tmp32 = tl.sigmoid(tmp31)
    tmp33 = tmp30 + tmp32
    tmp34 = tmp22 + tmp33
    tmp36 = tl.sigmoid(tmp35)
    tmp38 = tl.sigmoid(tmp37)
    tmp39 = tmp36 + tmp38
    tmp41 = tl.sigmoid(tmp40)
    tmp42 = tmp39 + tmp41
    tmp44 = tl.sigmoid(tmp43)
    tmp45 = tmp42 + tmp44
    tmp46 = tmp34 + tmp45
    tl.store(out_ptr0 + x0, tmp46, xmask)


@triton.jit
def triton_poi_fused_div_mul_sigmoid_sum_1(in_ptr0, in_ptr1, in_ptr2,
    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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x1), xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (4 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp13 = tl.load(in_ptr1 + (8 + x0 + 16 * x1), xmask)
    tmp18 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp19 = tl.load(in_ptr1 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp4 = tmp2 / tmp3
    tmp5 = tmp0 * tmp4
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tmp8 / tmp3
    tmp10 = tmp6 * tmp9
    tmp11 = tmp5 + tmp10
    tmp14 = tl.sigmoid(tmp13)
    tmp15 = tmp14 / tmp3
    tmp16 = tmp12 * tmp15
    tmp17 = tmp11 + tmp16
    tmp20 = tl.sigmoid(tmp19)
    tmp21 = tmp20 / tmp3
    tmp22 = tmp18 * tmp21
    tmp23 = tmp17 + tmp22
    tl.store(out_ptr0 + x2, tmp23, xmask)


@triton.jit
def triton_poi_fused_sum_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + x0, tmp6, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sigmoid_sum_0[grid(16)](arg0_1, buf0, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 64, 1), torch.float32)
        triton_poi_fused_div_mul_sigmoid_sum_1[grid(64)](arg1_1, arg0_1,
            buf0, buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf2 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        triton_poi_fused_sum_2[grid(16)](buf1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf1
    return buf2,


class PcamPoolNew(nn.Module):

    def __init__(self):
        super(PcamPoolNew, self).__init__()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
