# 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_6/inductor_cache/kj/ckjghs6qf23rivxzk4odx77h2zd6agxagizkq5mfrr7yb6izuqbt.py
# Topologically Sorted Source Nodes: [sum_1, spatial_sum, F_mean, sub, pow_1, sum_3, sum_4, F_variance, pow_2, adaptive_avg_pool2d, y], Original ATen: [aten.sum, aten.div, aten.sub, aten.pow, aten.mean, aten.add]
# Source node to ATen node mapping:
#   F_mean => div
#   F_variance => div_1
#   adaptive_avg_pool2d => mean
#   pow_1 => pow_1
#   pow_2 => pow_2
#   spatial_sum => sum_2
#   sub => sub
#   sum_1 => sum_1
#   sum_3 => sum_3
#   sum_4 => sum_4
#   y => add
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%primals_1, [3], True), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_1, [2], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, 16), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %div), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [3], True), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_3, [2], True), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_4, 16), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%div_1, 0.5), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [-1, -2], True), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_2, %mean), kwargs = {})
triton_per_fused_add_div_mean_pow_sub_sum_0 = async_compile.triton('triton_per_fused_add_div_mean_pow_sub_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.persistent_reduction(
    size_hints=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 17, 'num_reduction': 1, '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_pow_sub_sum_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp5 = tl.load(in_ptr0 + (16*x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp11 = tmp9 + tmp10
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp11 + tmp18
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 + tmp25
    tmp27 = tmp19 + tmp26
    tmp30 = tmp28 + tmp29
    tmp32 = tmp30 + tmp31
    tmp34 = tmp32 + tmp33
    tmp35 = tmp27 + tmp34
    tmp36 = 0.0625
    tmp37 = tmp35 * tmp36
    tmp38 = tmp5 - tmp37
    tmp39 = tmp38 * tmp38
    tmp40 = tmp6 - tmp37
    tmp41 = tmp40 * tmp40
    tmp42 = tmp39 + tmp41
    tmp43 = tmp8 - tmp37
    tmp44 = tmp43 * tmp43
    tmp45 = tmp42 + tmp44
    tmp46 = tmp10 - tmp37
    tmp47 = tmp46 * tmp46
    tmp48 = tmp45 + tmp47
    tmp49 = tmp12 - tmp37
    tmp50 = tmp49 * tmp49
    tmp51 = tmp13 - tmp37
    tmp52 = tmp51 * tmp51
    tmp53 = tmp50 + tmp52
    tmp54 = tmp15 - tmp37
    tmp55 = tmp54 * tmp54
    tmp56 = tmp53 + tmp55
    tmp57 = tmp17 - tmp37
    tmp58 = tmp57 * tmp57
    tmp59 = tmp56 + tmp58
    tmp60 = tmp48 + tmp59
    tmp61 = tmp20 - tmp37
    tmp62 = tmp61 * tmp61
    tmp63 = tmp21 - tmp37
    tmp64 = tmp63 * tmp63
    tmp65 = tmp62 + tmp64
    tmp66 = tmp23 - tmp37
    tmp67 = tmp66 * tmp66
    tmp68 = tmp65 + tmp67
    tmp69 = tmp25 - tmp37
    tmp70 = tmp69 * tmp69
    tmp71 = tmp68 + tmp70
    tmp72 = tmp60 + tmp71
    tmp73 = tmp28 - tmp37
    tmp74 = tmp73 * tmp73
    tmp75 = tmp29 - tmp37
    tmp76 = tmp75 * tmp75
    tmp77 = tmp74 + tmp76
    tmp78 = tmp31 - tmp37
    tmp79 = tmp78 * tmp78
    tmp80 = tmp77 + tmp79
    tmp81 = tmp33 - tmp37
    tmp82 = tmp81 * tmp81
    tmp83 = tmp80 + tmp82
    tmp84 = tmp72 + tmp83
    tmp85 = tmp84 * tmp36
    tmp86 = libdevice.sqrt(tmp85)
    tmp87 = 16.0
    tmp88 = tmp4 / tmp87
    tmp89 = tmp86 + tmp88
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp89, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/bp/cbpvdw3mp6xeofkt2trxpj2olsonpd5mtuzpi75qzm34orwv4vbo.py
# Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   sigmoid => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_sigmoid_1 = async_compile.triton('triton_poi_fused_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_1(in_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_out_ptr0 + (x0), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(in_out_ptr0 + (x0), tmp1, 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, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, 1, 3, 3), (9, 9, 3, 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)
        buf1 = buf0; del buf0  # reuse
        buf3 = reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [sum_1, spatial_sum, F_mean, sub, pow_1, sum_3, sum_4, F_variance, pow_2, adaptive_avg_pool2d, y], Original ATen: [aten.sum, aten.div, aten.sub, aten.pow, aten.mean, aten.add]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_mean_pow_sub_sum_0.run(buf3, primals_1, 16, 16, grid=grid(16), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 1, 1, 1), (1, 1, 1, 1))
        # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 1, 1), (4, 1, 1, 1))
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_1.run(buf6, 16, grid=grid(16), stream=stream0)
    return (buf6, primals_2, primals_3, buf3, buf4, 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, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 3, 3), (9, 9, 3, 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


def mean_channels(F):
    assert F.dim() == 4
    spatial_sum = F.sum(3, keepdim=True).sum(2, keepdim=True)
    return spatial_sum / (F.size(2) * F.size(3))


def stdv_channels(F):
    assert F.dim() == 4
    F_mean = mean_channels(F)
    F_variance = (F - F_mean).pow(2).sum(3, keepdim=True).sum(2, keepdim=True
        ) / (F.size(2) * F.size(3))
    return F_variance.pow(0.5)


def activation(act_type, inplace=False, neg_slope=0.05, n_prelu=1):
    act_type = act_type.lower()
    if act_type == 'relu':
        layer = nn.ReLU(inplace)
    elif act_type == 'lrelu':
        layer = nn.LeakyReLU(neg_slope, inplace)
    elif act_type == 'prelu':
        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
    else:
        raise NotImplementedError('activation layer [{:s}] is not found'.
            format(act_type))
    return layer


class LCCALayer(nn.Module):

    def __init__(self, channel):
        super(LCCALayer, self).__init__()
        self.contrast = stdv_channels
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.c3 = nn.Conv2d(channel, channel // 4, kernel_size=3, padding=(
            3 - 1) // 2, bias=False)
        self.c32 = nn.Conv2d(channel // 4, channel, kernel_size=3, padding=
            (3 - 1) // 2, bias=False)
        self.act = activation('relu')
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        y = self.contrast(x) + self.avg_pool(x)
        y = self.c32(self.c3(y))
        return self.sigmoid(y)


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


def get_init_inputs():
    return [[], {'channel': 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.triton_helpers import libdevice
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_per_fused_add_div_mean_pow_sub_sum_0(in_out_ptr0, in_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp5 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp29 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp31 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp33 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp11 = tmp9 + tmp10
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp11 + tmp18
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 + tmp25
    tmp27 = tmp19 + tmp26
    tmp30 = tmp28 + tmp29
    tmp32 = tmp30 + tmp31
    tmp34 = tmp32 + tmp33
    tmp35 = tmp27 + tmp34
    tmp36 = 0.0625
    tmp37 = tmp35 * tmp36
    tmp38 = tmp5 - tmp37
    tmp39 = tmp38 * tmp38
    tmp40 = tmp6 - tmp37
    tmp41 = tmp40 * tmp40
    tmp42 = tmp39 + tmp41
    tmp43 = tmp8 - tmp37
    tmp44 = tmp43 * tmp43
    tmp45 = tmp42 + tmp44
    tmp46 = tmp10 - tmp37
    tmp47 = tmp46 * tmp46
    tmp48 = tmp45 + tmp47
    tmp49 = tmp12 - tmp37
    tmp50 = tmp49 * tmp49
    tmp51 = tmp13 - tmp37
    tmp52 = tmp51 * tmp51
    tmp53 = tmp50 + tmp52
    tmp54 = tmp15 - tmp37
    tmp55 = tmp54 * tmp54
    tmp56 = tmp53 + tmp55
    tmp57 = tmp17 - tmp37
    tmp58 = tmp57 * tmp57
    tmp59 = tmp56 + tmp58
    tmp60 = tmp48 + tmp59
    tmp61 = tmp20 - tmp37
    tmp62 = tmp61 * tmp61
    tmp63 = tmp21 - tmp37
    tmp64 = tmp63 * tmp63
    tmp65 = tmp62 + tmp64
    tmp66 = tmp23 - tmp37
    tmp67 = tmp66 * tmp66
    tmp68 = tmp65 + tmp67
    tmp69 = tmp25 - tmp37
    tmp70 = tmp69 * tmp69
    tmp71 = tmp68 + tmp70
    tmp72 = tmp60 + tmp71
    tmp73 = tmp28 - tmp37
    tmp74 = tmp73 * tmp73
    tmp75 = tmp29 - tmp37
    tmp76 = tmp75 * tmp75
    tmp77 = tmp74 + tmp76
    tmp78 = tmp31 - tmp37
    tmp79 = tmp78 * tmp78
    tmp80 = tmp77 + tmp79
    tmp81 = tmp33 - tmp37
    tmp82 = tmp81 * tmp81
    tmp83 = tmp80 + tmp82
    tmp84 = tmp72 + tmp83
    tmp85 = tmp84 * tmp36
    tmp86 = libdevice.sqrt(tmp85)
    tmp87 = 16.0
    tmp88 = tmp4 / tmp87
    tmp89 = tmp86 + tmp88
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp89, xmask)


@triton.jit
def triton_poi_fused_sigmoid_1(in_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_out_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, 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, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, 1, 3, 3), (9, 9, 3, 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)
        buf1 = buf0
        del buf0
        buf3 = reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_div_mean_pow_sub_sum_0[grid(16)](buf3,
            primals_1, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_1
        buf4 = extern_kernels.convolution(buf3, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 1, 1, 1), (1, 1, 1, 1))
        buf5 = extern_kernels.convolution(buf4, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 1, 1), (4, 1, 1, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_sigmoid_1[grid(16)](buf6, 16, XBLOCK=16, num_warps
            =1, num_stages=1)
    return buf6, primals_2, primals_3, buf3, buf4, buf6


def mean_channels(F):
    assert F.dim() == 4
    spatial_sum = F.sum(3, keepdim=True).sum(2, keepdim=True)
    return spatial_sum / (F.size(2) * F.size(3))


def stdv_channels(F):
    assert F.dim() == 4
    F_mean = mean_channels(F)
    F_variance = (F - F_mean).pow(2).sum(3, keepdim=True).sum(2, keepdim=True
        ) / (F.size(2) * F.size(3))
    return F_variance.pow(0.5)


def activation(act_type, inplace=False, neg_slope=0.05, n_prelu=1):
    act_type = act_type.lower()
    if act_type == 'relu':
        layer = nn.ReLU(inplace)
    elif act_type == 'lrelu':
        layer = nn.LeakyReLU(neg_slope, inplace)
    elif act_type == 'prelu':
        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
    else:
        raise NotImplementedError('activation layer [{:s}] is not found'.
            format(act_type))
    return layer


class LCCALayerNew(nn.Module):

    def __init__(self, channel):
        super(LCCALayerNew, self).__init__()
        self.contrast = stdv_channels
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.c3 = nn.Conv2d(channel, channel // 4, kernel_size=3, padding=(
            3 - 1) // 2, bias=False)
        self.c32 = nn.Conv2d(channel // 4, channel, kernel_size=3, padding=
            (3 - 1) // 2, bias=False)
        self.act = activation('relu')
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_0):
        primals_2 = self.c3.weight
        primals_3 = self.c32.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
