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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.optim
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_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 52272
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 66
    x1 = xindex // 66 % 66
    x2 = xindex // 4356
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
        x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 4096 * x2),
        xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4096 % 128
    x0 = xindex % 4096
    x2 = xindex // 524288
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 64, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4096 * x1 + 262144 * x2), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 128, tl.int64)
    tmp15 = tl.load(in_ptr2 + (x0 + 4096 * (-64 + x1) + 262144 * x2), tmp12,
        other=0.0)
    tmp16 = tl.load(in_ptr3 + (-64 + x1), tmp12, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + x3, tmp21, None)


@triton.jit
def triton_poi_fused_reflection_pad2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 66
    x1 = xindex // 66 % 66
    x2 = xindex // 4356
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
        x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 4096 * x2),
        None, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, None)


@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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, None)


@triton.jit
def triton_red_fused_convolution_mean_4(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 12
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x3 = xindex
    x0 = xindex % 3
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    _tmp4 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_out_ptr0 + (r2 + 4096 * x3), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp5 = _tmp4 + tmp3
        _tmp4 = tl.where(rmask & xmask, tmp5, _tmp4)
        tl.store(in_out_ptr0 + (r2 + 4096 * x3), tmp2, rmask & xmask)
    tmp4 = tl.sum(_tmp4, 1)[:, None]
    tmp6 = 4096.0
    tmp7 = tmp4 / tmp6
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_add_mul_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x1 = xindex // 4096
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x2, None)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + x2, tmp5, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_6(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 64
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26) = args
    args.clear()
    assert_size_stride(primals_1, (64, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64, 128, 5, 5), (3200, 25, 5, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (64,), (1,))
    assert_size_stride(primals_12, (3, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_13, (3,), (1,))
    assert_size_stride(primals_14, (1, 1, 3), (3, 3, 1))
    assert_size_stride(primals_15, (64, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_16, (64,), (1,))
    assert_size_stride(primals_17, (64, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_18, (64,), (1,))
    assert_size_stride(primals_19, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_20, (64,), (1,))
    assert_size_stride(primals_21, (64, 128, 5, 5), (3200, 25, 5, 1))
    assert_size_stride(primals_22, (64,), (1,))
    assert_size_stride(primals_23, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_24, (64,), (1,))
    assert_size_stride(primals_25, (3, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_26, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 3, 66, 66), (13068, 4356, 66, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(52272)](primals_3, buf1, 
            52272, XBLOCK=256, num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf3 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_1[grid(2097152)](buf0, primals_2, buf2,
            primals_5, buf3, 2097152, XBLOCK=1024, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf5 = empty_strided_cuda((4, 128, 66, 66), (557568, 4356, 66, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_2[grid(2230272)](buf3, buf5, 
            2230272, XBLOCK=512, num_warps=8, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf7 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_1[grid(2097152)](buf4, primals_7, buf6,
            primals_9, buf7, 2097152, XBLOCK=1024, num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_3[grid(1048576)](buf9, primals_11,
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_11
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf11 = buf10
        del buf10
        buf12 = empty_strided_cuda((4, 3, 1, 1), (3, 1, 12, 12), torch.float32)
        buf13 = buf12
        del buf12
        triton_red_fused_convolution_mean_4[grid(12)](buf11, buf13,
            primals_13, 12, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        del primals_13
        buf14 = extern_kernels.convolution(reinterpret_tensor(buf13, (4, 1,
            3), (3, 0, 1), 0), primals_14, stride=(1,), padding=(1,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf14, (4, 1, 3), (3, 3, 1))
        buf15 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_add_mul_5[grid(49152)](buf11, buf14, primals_3,
            buf15, 49152, XBLOCK=512, num_warps=4, num_stages=1)
        buf16 = extern_kernels.convolution(buf15, primals_15, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf17 = empty_strided_cuda((4, 3, 66, 66), (13068, 4356, 66, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_0[grid(52272)](buf15, buf17, 
            52272, XBLOCK=256, num_warps=4, num_stages=1)
        buf18 = extern_kernels.convolution(buf17, primals_17, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf19 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_1[grid(2097152)](buf16, primals_16, buf18,
            primals_18, buf19, 2097152, XBLOCK=1024, num_warps=4, num_stages=1)
        buf20 = extern_kernels.convolution(buf19, primals_19, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf21 = empty_strided_cuda((4, 128, 66, 66), (557568, 4356, 66, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_2[grid(2230272)](buf19, buf21, 
            2230272, XBLOCK=512, num_warps=8, num_stages=1)
        buf22 = extern_kernels.convolution(buf21, primals_21, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf23 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_cat_1[grid(2097152)](buf20, primals_20, buf22,
            primals_22, buf23, 2097152, XBLOCK=1024, num_warps=4, num_stages=1)
        buf24 = extern_kernels.convolution(buf23, primals_23, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_relu_3[grid(1048576)](buf25,
            primals_24, 1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_24
        buf26 = extern_kernels.convolution(buf25, primals_25, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf27 = buf26
        del buf26
        buf28 = empty_strided_cuda((4, 3, 1, 1), (3, 1, 12, 12), torch.float32)
        buf29 = buf28
        del buf28
        triton_red_fused_convolution_mean_4[grid(12)](buf27, buf29,
            primals_26, 12, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        del primals_26
        buf30 = extern_kernels.convolution(reinterpret_tensor(buf29, (4, 1,
            3), (3, 0, 1), 0), primals_14, stride=(1,), padding=(1,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf30, (4, 1, 3), (3, 3, 1))
        buf31 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_add_mul_5[grid(49152)](buf27, buf30, buf15, buf31,
            49152, XBLOCK=512, num_warps=4, num_stages=1)
        buf32 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
            buf22, primals_22, buf32, 1048576, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf22
        del primals_22
        buf33 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
            buf20, primals_20, buf33, 1048576, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf20
        del primals_20
        buf34 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
            buf18, primals_18, buf34, 1048576, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf18
        del primals_18
        buf35 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
            buf16, primals_16, buf35, 1048576, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf16
        del primals_16
        buf36 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
            buf6, primals_9, buf36, 1048576, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf6
        del primals_9
        buf37 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
            buf4, primals_7, buf37, 1048576, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf4
        del primals_7
        buf38 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
            buf2, primals_5, buf38, 1048576, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf2
        del primals_5
        buf39 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(1048576)](
            buf0, primals_2, buf39, 1048576, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf0
        del primals_2
    return (buf31, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, primals_12, primals_14, primals_15, primals_17,
        primals_19, primals_21, primals_23, primals_25, buf1, buf3, buf5,
        buf7, buf9, buf11, reinterpret_tensor(buf13, (4, 1, 3), (3, 1, 1), 
        0), buf14, buf15, buf17, buf19, buf21, buf23, buf25, buf27,
        reinterpret_tensor(buf29, (4, 1, 3), (3, 1, 1), 0), buf30, buf32,
        buf33, buf34, buf35, buf36, buf37, buf38, buf39)


class eca_layer(nn.Module):
    """Constructs a ECA module.
    Args:
        channel: Number of channels of the input feature map
        k_size: Adaptive selection of kernel size
    """

    def __init__(self, channel, k_size=3):
        super(eca_layer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1
            ) // 2, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        _b, _c, _h, _w = x.size()
        y = self.avg_pool(x)
        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2
            ).unsqueeze(-1)
        y = self.sigmoid(y)
        return x * y.expand_as(x)


class SRNetNew(nn.Module):

    def __init__(self):
        super(SRNetNew, self).__init__()
        self.relu = nn.ReLU(inplace=True)
        self.pad = nn.ReflectionPad2d(1)
        self.Conv1 = nn.Conv2d(3, 64, 3, 1, 1, bias=True)
        self.Conv1_ = nn.Conv2d(3, 64, 5, 1, 1, bias=True)
        self.Conv2 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
        self.Conv2_ = nn.Conv2d(128, 64, 5, 1, 1, bias=True)
        self.Conv3 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
        self.Conv4 = nn.Conv2d(64, 3, 3, 1, 1, bias=True)
        self.Conv5 = nn.Conv2d(3, 64, 3, 1, 1, bias=True)
        self.Conv5_ = nn.Conv2d(3, 64, 5, 1, 1, bias=True)
        self.Conv6 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
        self.Conv6_ = nn.Conv2d(128, 64, 5, 1, 1, bias=True)
        self.Conv7 = nn.Conv2d(128, 64, 3, 1, 1, bias=True)
        self.Conv8 = nn.Conv2d(64, 3, 3, 1, 1, bias=True)
        self.eca = eca_layer(3, 3)

    def forward(self, input_0):
        primals_1 = self.Conv1.weight
        primals_2 = self.Conv1.bias
        primals_4 = self.Conv1_.weight
        primals_5 = self.Conv1_.bias
        primals_6 = self.Conv2.weight
        primals_7 = self.Conv2.bias
        primals_8 = self.Conv2_.weight
        primals_9 = self.Conv2_.bias
        primals_10 = self.Conv3.weight
        primals_11 = self.Conv3.bias
        primals_12 = self.Conv4.weight
        primals_13 = self.Conv4.bias
        primals_15 = self.Conv5.weight
        primals_16 = self.Conv5.bias
        primals_17 = self.Conv5_.weight
        primals_18 = self.Conv5_.bias
        primals_19 = self.Conv6.weight
        primals_20 = self.Conv6.bias
        primals_21 = self.Conv6_.weight
        primals_22 = self.Conv6_.bias
        primals_23 = self.Conv7.weight
        primals_24 = self.Conv7.bias
        primals_25 = self.Conv8.weight
        primals_26 = self.Conv8.bias
        primals_14 = self.eca.conv.weight
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26])
        return output[0]


Measured latency_ms: 0.039001