# 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_1/inductor_cache/2u/c2udrypr3ijcffpewjzpng56qrgmliz5n5yvcl52crrm77htfjsq.py
# Topologically Sorted Source Nodes: [stack, alpha, max_pool2d, mul, sub, avg_pool2d, mul_1, add], Original ATen: [aten.stack, aten.sigmoid, aten.max_pool2d_with_indices, aten.mul, aten.rsub, aten.avg_pool2d, aten.add]
# Source node to ATen node mapping:
#   add => add
#   alpha => sigmoid
#   avg_pool2d => avg_pool2d
#   max_pool2d => getitem_4
#   mul => mul
#   mul_1 => mul_1
#   stack => cat
#   sub => sub
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%squeeze, %squeeze_1, %squeeze_2, %squeeze_3], 1), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view,), kwargs = {})
#   %getitem_4 : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %getitem_4), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %avg_pool2d : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%primals_1, [4, 4], [4, 4]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %avg_pool2d), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
triton_poi_fused_add_avg_pool2d_max_pool2d_with_indices_mul_rsub_sigmoid_stack_0 = async_compile.triton('triton_poi_fused_add_avg_pool2d_max_pool2d_with_indices_mul_rsub_sigmoid_stack_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_avg_pool2d_max_pool2d_with_indices_mul_rsub_sigmoid_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 21, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_avg_pool2d_max_pool2d_with_indices_mul_rsub_sigmoid_stack_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp6 = tl.load(in_ptr1 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp37 = tl.load(in_ptr5 + (16*x2), xmask, eviction_policy='evict_last')
    tmp38 = tl.load(in_ptr5 + (1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr5 + (2 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr5 + (3 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr5 + (4 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp46 = tl.load(in_ptr5 + (5 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr5 + (6 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp50 = tl.load(in_ptr5 + (7 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp52 = tl.load(in_ptr5 + (8 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp54 = tl.load(in_ptr5 + (9 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp56 = tl.load(in_ptr5 + (10 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp58 = tl.load(in_ptr5 + (11 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp60 = tl.load(in_ptr5 + (12 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp62 = tl.load(in_ptr5 + (13 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp64 = tl.load(in_ptr5 + (14 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp66 = tl.load(in_ptr5 + (15 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp0 = x0
    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 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp5 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tmp12 = tl.full([1], 2, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x1), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp15 + tmp7
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp14, tmp16, tmp17)
    tmp19 = tmp0 >= tmp12
    tmp20 = tl.full([1], 3, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr3 + (x1), tmp22 & xmask, eviction_policy='evict_last', other=0.0)
    tmp24 = tmp23 + tmp7
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp22, tmp24, tmp25)
    tmp27 = tmp0 >= tmp20
    tmp28 = tl.full([1], 4, tl.int64)
    tmp29 = tmp0 < tmp28
    tmp30 = tl.load(in_ptr4 + (x1), tmp27 & xmask, eviction_policy='evict_last', other=0.0)
    tmp31 = tmp30 + tmp7
    tmp32 = tl.full(tmp31.shape, 0.0, tmp31.dtype)
    tmp33 = tl.where(tmp27, tmp31, tmp32)
    tmp34 = tl.where(tmp22, tmp26, tmp33)
    tmp35 = tl.where(tmp14, tmp18, tmp34)
    tmp36 = tl.where(tmp4, tmp10, tmp35)
    tmp39 = triton_helpers.maximum(tmp38, tmp37)
    tmp41 = triton_helpers.maximum(tmp40, tmp39)
    tmp43 = triton_helpers.maximum(tmp42, tmp41)
    tmp45 = triton_helpers.maximum(tmp44, tmp43)
    tmp47 = triton_helpers.maximum(tmp46, tmp45)
    tmp49 = triton_helpers.maximum(tmp48, tmp47)
    tmp51 = triton_helpers.maximum(tmp50, tmp49)
    tmp53 = triton_helpers.maximum(tmp52, tmp51)
    tmp55 = triton_helpers.maximum(tmp54, tmp53)
    tmp57 = triton_helpers.maximum(tmp56, tmp55)
    tmp59 = triton_helpers.maximum(tmp58, tmp57)
    tmp61 = triton_helpers.maximum(tmp60, tmp59)
    tmp63 = triton_helpers.maximum(tmp62, tmp61)
    tmp65 = triton_helpers.maximum(tmp64, tmp63)
    tmp67 = triton_helpers.maximum(tmp66, tmp65)
    tmp68 = tmp38 + tmp37
    tmp69 = tmp40 + tmp68
    tmp70 = tmp42 + tmp69
    tmp71 = tmp44 + tmp70
    tmp72 = tmp46 + tmp71
    tmp73 = tmp48 + tmp72
    tmp74 = tmp50 + tmp73
    tmp75 = tmp52 + tmp74
    tmp76 = tmp54 + tmp75
    tmp77 = tmp56 + tmp76
    tmp78 = tmp58 + tmp77
    tmp79 = tmp60 + tmp78
    tmp80 = tmp62 + tmp79
    tmp81 = tmp64 + tmp80
    tmp82 = tmp66 + tmp81
    tmp83 = 0.0625
    tmp84 = tmp82 * tmp83
    tmp85 = tl.sigmoid(tmp36)
    tmp86 = tmp85 * tmp67
    tmp87 = 1.0
    tmp88 = tmp87 - tmp85
    tmp89 = tmp88 * tmp84
    tmp90 = tmp86 + tmp89
    tl.store(out_ptr0 + (x2), tmp36, xmask)
    tl.store(out_ptr1 + (x2), tmp67, xmask)
    tl.store(out_ptr2 + (x2), tmp84, xmask)
    tl.store(out_ptr3 + (x2), tmp90, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 0), primals_2, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 1, 1), (1, 1, 1, 1))
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 16), primals_2, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 1, 1), (1, 1, 1, 1))
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 32), primals_2, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 1, 1), (1, 1, 1, 1))
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 48), primals_2, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 1, 1), (1, 1, 1, 1))
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [stack, alpha, max_pool2d, mul, sub, avg_pool2d, mul_1, add], Original ATen: [aten.stack, aten.sigmoid, aten.max_pool2d_with_indices, aten.mul, aten.rsub, aten.avg_pool2d, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_avg_pool2d_max_pool2d_with_indices_mul_rsub_sigmoid_stack_0.run(buf0, primals_3, buf1, buf2, buf3, primals_1, buf4, buf5, buf6, buf7, 16, grid=grid(16), stream=stream0)
        del buf0
        del buf1
        del buf2
        del buf3
        del primals_3
    return (buf7, primals_2, reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 16), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 32), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 48), buf4, buf5, buf6, )


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, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 GatedPooling1(nn.Module):
    """
        Gated pooling as defined in https://arxiv.org/abs/1509.08985
        This implementation is the L variant ( entire layer, one parameter )
    """

    def __init__(self, kernel_size):
        super(GatedPooling1, self).__init__()
        self.avgpool = nn.AvgPool2d(kernel_size)
        self.maxpool = nn.MaxPool2d(kernel_size)
        self.transform = nn.Conv2d(1, 1, kernel_size=kernel_size, stride=
            kernel_size)
        torch.nn.init.kaiming_normal_(self.transform.weight)

    def forward(self, x):
        xs = [self.transform(x_filt.unsqueeze(1)).squeeze(1) for x_filt in
            torch.unbind(x, dim=1)]
        alpha = torch.sigmoid(torch.stack(xs, 1))
        return alpha * self.maxpool(x) + (1 - alpha) * self.avgpool(x)


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


def get_init_inputs():
    return [[], {'kernel_size': 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_avg_pool2d_max_pool2d_with_indices_mul_rsub_sigmoid_stack_0(
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0,
    out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp6 = tl.load(in_ptr1 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp37 = tl.load(in_ptr5 + 16 * x2, xmask, eviction_policy='evict_last')
    tmp38 = tl.load(in_ptr5 + (1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp40 = tl.load(in_ptr5 + (2 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp42 = tl.load(in_ptr5 + (3 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp44 = tl.load(in_ptr5 + (4 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp46 = tl.load(in_ptr5 + (5 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp48 = tl.load(in_ptr5 + (6 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp50 = tl.load(in_ptr5 + (7 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp52 = tl.load(in_ptr5 + (8 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp54 = tl.load(in_ptr5 + (9 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp56 = tl.load(in_ptr5 + (10 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp58 = tl.load(in_ptr5 + (11 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp60 = tl.load(in_ptr5 + (12 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp62 = tl.load(in_ptr5 + (13 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp64 = tl.load(in_ptr5 + (14 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp66 = tl.load(in_ptr5 + (15 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp8 = tmp5 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tmp12 = tl.full([1], 2, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + x1, tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp15 + tmp7
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp14, tmp16, tmp17)
    tmp19 = tmp0 >= tmp12
    tmp20 = tl.full([1], 3, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr3 + x1, tmp22 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp24 = tmp23 + tmp7
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp22, tmp24, tmp25)
    tmp27 = tmp0 >= tmp20
    tl.full([1], 4, tl.int64)
    tmp30 = tl.load(in_ptr4 + x1, tmp27 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp31 = tmp30 + tmp7
    tmp32 = tl.full(tmp31.shape, 0.0, tmp31.dtype)
    tmp33 = tl.where(tmp27, tmp31, tmp32)
    tmp34 = tl.where(tmp22, tmp26, tmp33)
    tmp35 = tl.where(tmp14, tmp18, tmp34)
    tmp36 = tl.where(tmp4, tmp10, tmp35)
    tmp39 = triton_helpers.maximum(tmp38, tmp37)
    tmp41 = triton_helpers.maximum(tmp40, tmp39)
    tmp43 = triton_helpers.maximum(tmp42, tmp41)
    tmp45 = triton_helpers.maximum(tmp44, tmp43)
    tmp47 = triton_helpers.maximum(tmp46, tmp45)
    tmp49 = triton_helpers.maximum(tmp48, tmp47)
    tmp51 = triton_helpers.maximum(tmp50, tmp49)
    tmp53 = triton_helpers.maximum(tmp52, tmp51)
    tmp55 = triton_helpers.maximum(tmp54, tmp53)
    tmp57 = triton_helpers.maximum(tmp56, tmp55)
    tmp59 = triton_helpers.maximum(tmp58, tmp57)
    tmp61 = triton_helpers.maximum(tmp60, tmp59)
    tmp63 = triton_helpers.maximum(tmp62, tmp61)
    tmp65 = triton_helpers.maximum(tmp64, tmp63)
    tmp67 = triton_helpers.maximum(tmp66, tmp65)
    tmp68 = tmp38 + tmp37
    tmp69 = tmp40 + tmp68
    tmp70 = tmp42 + tmp69
    tmp71 = tmp44 + tmp70
    tmp72 = tmp46 + tmp71
    tmp73 = tmp48 + tmp72
    tmp74 = tmp50 + tmp73
    tmp75 = tmp52 + tmp74
    tmp76 = tmp54 + tmp75
    tmp77 = tmp56 + tmp76
    tmp78 = tmp58 + tmp77
    tmp79 = tmp60 + tmp78
    tmp80 = tmp62 + tmp79
    tmp81 = tmp64 + tmp80
    tmp82 = tmp66 + tmp81
    tmp83 = 0.0625
    tmp84 = tmp82 * tmp83
    tmp85 = tl.sigmoid(tmp36)
    tmp86 = tmp85 * tmp67
    tmp87 = 1.0
    tmp88 = tmp87 - tmp85
    tmp89 = tmp88 * tmp84
    tmp90 = tmp86 + tmp89
    tl.store(out_ptr0 + x2, tmp36, xmask)
    tl.store(out_ptr1 + x2, tmp67, xmask)
    tl.store(out_ptr2 + x2, tmp84, xmask)
    tl.store(out_ptr3 + x2, tmp90, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4, 4), (64, 0, 4, 1), 0), primals_2, stride=(4, 4), padding=
            (0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0
            ), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 1, 1), (1, 1, 1, 1))
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4, 4), (64, 0, 4, 1), 16), primals_2, stride=(4, 4), padding
            =(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 1, 1), (1, 1, 1, 1))
        buf2 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4, 4), (64, 0, 4, 1), 32), primals_2, stride=(4, 4), padding
            =(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 1, 1), (1, 1, 1, 1))
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4, 4), (64, 0, 4, 1), 48), primals_2, stride=(4, 4), padding
            =(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 1, 1), (1, 1, 1, 1))
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_avg_pool2d_max_pool2d_with_indices_mul_rsub_sigmoid_stack_0[
            grid(16)](buf0, primals_3, buf1, buf2, buf3, primals_1, buf4,
            buf5, buf6, buf7, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del buf2
        del buf3
        del primals_3
    return buf7, primals_2, reinterpret_tensor(primals_1, (4, 1, 4, 4), (64,
        16, 4, 1), 0), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16,
        4, 1), 16), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4,
        1), 32), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 48
        ), buf4, buf5, buf6


class GatedPooling1New(nn.Module):
    """
        Gated pooling as defined in https://arxiv.org/abs/1509.08985
        This implementation is the L variant ( entire layer, one parameter )
    """

    def __init__(self, kernel_size):
        super(GatedPooling1New, self).__init__()
        self.avgpool = nn.AvgPool2d(kernel_size)
        self.maxpool = nn.MaxPool2d(kernel_size)
        self.transform = nn.Conv2d(1, 1, kernel_size=kernel_size, stride=
            kernel_size)
        torch.nn.init.kaiming_normal_(self.transform.weight)

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