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 libdevice
from torch.autograd import Function
import math
import torch.nn as nn
import torch.utils.data
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 576
    xnumel = 81
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 81 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 243 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 12
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 25
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 192
    y1 = yindex // 192
    tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 192 * x2 + 4800 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 192
    y1 = yindex // 192
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 192 * x2 + 1728 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_pow_4(in_out_ptr0, 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)
    x2 = xindex
    x0 = xindex % 192
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tmp2 * tmp2
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(out_ptr0 + x2, tmp3, None)


@triton.jit
def triton_poi_fused_maximum_mul_pow_sub_5(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
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 3.814697265625e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp2 * tmp2
    tmp4 = 1.4551915228366852e-11
    tmp5 = tmp3 - tmp4
    tl.store(out_ptr0 + x0, tmp5, None)


@triton.jit
def triton_poi_fused_convolution_div_maximum_mul_pow_sqrt_sub_6(in_out_ptr0,
    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)
    x2 = xindex
    x0 = xindex % 192
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + x2, None)
    tmp2 = 0.0010000072652474046
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp3 * tmp3
    tmp5 = 1.4551915228366852e-11
    tmp6 = tmp4 - tmp5
    tmp7 = tmp0 + tmp6
    tmp9 = libdevice.sqrt(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + x2, tmp7, None)
    tl.store(out_ptr0 + x2, tmp10, None)


@triton.jit
def triton_poi_fused_convolution_pow_7(in_out_ptr0, 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)
    x2 = xindex
    x0 = xindex % 192
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tmp2 * tmp2
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(out_ptr0 + x2, tmp3, None)


@triton.jit
def triton_poi_fused_ge_maximum_mul_pow_sub_8(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 3.814697265625e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp2 * tmp2
    tmp4 = 1.4551915228366852e-11
    tmp5 = tmp3 - tmp4
    tmp6 = 2.0
    tmp7 = tmp2 * tmp6
    tmp8 = tmp0 >= tmp1
    tl.store(out_ptr0 + x0, tmp5, None)
    tl.store(out_ptr1 + x0, tmp7, None)
    tl.store(out_ptr2 + x0, tmp8, None)


@triton.jit
def triton_poi_fused_convolution_div_maximum_mul_pow_sqrt_sub_9(in_out_ptr0,
    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)
    x2 = xindex
    x0 = xindex % 192
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + x2, None)
    tmp2 = 0.0010000072652474046
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp3 * tmp3
    tmp5 = 1.4551915228366852e-11
    tmp6 = tmp4 - tmp5
    tmp7 = tmp0 + tmp6
    tmp9 = libdevice.sqrt(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + x2, tmp7, None)
    tl.store(out_ptr0 + x2, tmp10, None)


@triton.jit
def triton_poi_fused_convolution_10(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 768
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 192
    y1 = yindex // 192
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 192 * x2 + 3072 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_ge_maximum_mul_pow_11(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 192
    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 = 0.0010000072652474046
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 2.0
    tmp4 = tmp2 * tmp3
    tmp5 = tmp0 >= tmp1
    tl.store(out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


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) = args
    args.clear()
    assert_size_stride(primals_1, (192, 3, 9, 9), (243, 81, 9, 1))
    assert_size_stride(primals_2, (192,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (192,), (1,))
    assert_size_stride(primals_5, (192, 192), (192, 1))
    assert_size_stride(primals_6, (192, 192, 5, 5), (4800, 25, 5, 1))
    assert_size_stride(primals_7, (192,), (1,))
    assert_size_stride(primals_8, (192,), (1,))
    assert_size_stride(primals_9, (192, 192), (192, 1))
    assert_size_stride(primals_10, (192, 192, 3, 3), (1728, 9, 3, 1))
    assert_size_stride(primals_11, (192,), (1,))
    assert_size_stride(primals_12, (192,), (1,))
    assert_size_stride(primals_13, (192, 192), (192, 1))
    assert_size_stride(primals_14, (192, 192, 5, 5), (4800, 25, 5, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((192, 3, 9, 9), (243, 1, 27, 3), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(576, 81)](primals_1, buf0, 576, 81, XBLOCK=
            32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
            .float32)
        triton_poi_fused_1[grid(12, 4096)](primals_3, buf1, 12, 4096,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((192, 192, 5, 5), (4800, 1, 960, 192),
            torch.float32)
        triton_poi_fused_2[grid(36864, 25)](primals_6, buf2, 36864, 25,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_6
        buf3 = empty_strided_cuda((192, 192, 3, 3), (1728, 1, 576, 192),
            torch.float32)
        triton_poi_fused_3[grid(36864, 9)](primals_10, buf3, 36864, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_10
        buf4 = empty_strided_cuda((192, 192, 5, 5), (4800, 1, 960, 192),
            torch.float32)
        triton_poi_fused_2[grid(36864, 25)](primals_14, buf4, 36864, 25,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_14
        buf5 = extern_kernels.convolution(buf1, buf0, stride=(4, 4),
            padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 192, 16, 16), (49152, 1, 3072, 192))
        buf6 = buf5
        del buf5
        buf8 = empty_strided_cuda((4, 192, 16, 16), (49152, 1, 3072, 192),
            torch.float32)
        triton_poi_fused_convolution_pow_4[grid(196608)](buf6, primals_2,
            buf8, 196608, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf7 = empty_strided_cuda((192, 192), (192, 1), torch.float32)
        triton_poi_fused_maximum_mul_pow_sub_5[grid(36864)](primals_5, buf7,
            36864, XBLOCK=512, num_warps=4, num_stages=1)
        buf9 = extern_kernels.convolution(buf8, reinterpret_tensor(buf7, (
            192, 192, 1, 1), (192, 1, 0, 0), 0), stride=(1, 1), padding=(0,
            0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf9, (4, 192, 16, 16), (49152, 1, 3072, 192))
        buf10 = buf9
        del buf9
        buf11 = empty_strided_cuda((4, 192, 16, 16), (49152, 1, 3072, 192),
            torch.float32)
        triton_poi_fused_convolution_div_maximum_mul_pow_sqrt_sub_6[grid(
            196608)](buf10, primals_4, buf6, buf11, 196608, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf12 = extern_kernels.convolution(buf11, buf2, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 192, 8, 8), (12288, 1, 1536, 192))
        buf13 = buf12
        del buf12
        buf15 = empty_strided_cuda((4, 192, 8, 8), (12288, 1, 1536, 192),
            torch.float32)
        triton_poi_fused_convolution_pow_7[grid(49152)](buf13, primals_7,
            buf15, 49152, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_7
        buf14 = empty_strided_cuda((192, 192), (192, 1), torch.float32)
        buf32 = empty_strided_cuda((192, 192), (192, 1), torch.float32)
        buf33 = empty_strided_cuda((192, 192), (192, 1), torch.bool)
        triton_poi_fused_ge_maximum_mul_pow_sub_8[grid(36864)](primals_9,
            buf14, buf32, buf33, 36864, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_9
        buf16 = extern_kernels.convolution(buf15, reinterpret_tensor(buf14,
            (192, 192, 1, 1), (192, 1, 0, 0), 0), stride=(1, 1), padding=(0,
            0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf16, (4, 192, 8, 8), (12288, 1, 1536, 192))
        buf17 = buf16
        del buf16
        buf18 = empty_strided_cuda((4, 192, 8, 8), (12288, 1, 1536, 192),
            torch.float32)
        triton_poi_fused_convolution_div_maximum_mul_pow_sqrt_sub_9[grid(49152)
            ](buf17, primals_8, buf13, buf18, 49152, XBLOCK=512, num_warps=
            4, num_stages=1)
        buf19 = extern_kernels.convolution(buf18, buf3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 192, 8, 8), (12288, 1, 1536, 192))
        buf20 = buf19
        del buf19
        buf22 = empty_strided_cuda((4, 192, 8, 8), (12288, 1, 1536, 192),
            torch.float32)
        triton_poi_fused_convolution_pow_7[grid(49152)](buf20, primals_11,
            buf22, 49152, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_11
        buf21 = empty_strided_cuda((192, 192), (192, 1), torch.float32)
        buf28 = empty_strided_cuda((192, 192), (192, 1), torch.float32)
        buf29 = empty_strided_cuda((192, 192), (192, 1), torch.bool)
        triton_poi_fused_ge_maximum_mul_pow_sub_8[grid(36864)](primals_13,
            buf21, buf28, buf29, 36864, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_13
        buf23 = extern_kernels.convolution(buf22, reinterpret_tensor(buf21,
            (192, 192, 1, 1), (192, 1, 0, 0), 0), stride=(1, 1), padding=(0,
            0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf23, (4, 192, 8, 8), (12288, 1, 1536, 192))
        buf24 = buf23
        del buf23
        buf25 = empty_strided_cuda((4, 192, 8, 8), (12288, 1, 1536, 192),
            torch.float32)
        triton_poi_fused_convolution_div_maximum_mul_pow_sqrt_sub_9[grid(49152)
            ](buf24, primals_12, buf20, buf25, 49152, XBLOCK=512, num_warps
            =4, num_stages=1)
        buf26 = extern_kernels.convolution(buf25, buf4, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 192, 4, 4), (3072, 1, 768, 192))
        buf27 = empty_strided_cuda((4, 192, 4, 4), (3072, 16, 4, 1), torch.
            float32)
        triton_poi_fused_convolution_10[grid(768, 16)](buf26, buf27, 768, 
            16, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del buf26
        buf30 = empty_strided_cuda((192,), (1,), torch.float32)
        buf31 = empty_strided_cuda((192,), (1,), torch.bool)
        triton_poi_fused_ge_maximum_mul_pow_11[grid(192)](primals_12, buf30,
            buf31, 192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_12
        buf34 = empty_strided_cuda((192,), (1,), torch.float32)
        buf35 = empty_strided_cuda((192,), (1,), torch.bool)
        triton_poi_fused_ge_maximum_mul_pow_11[grid(192)](primals_8, buf34,
            buf35, 192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_8
    return (buf27, buf0, buf1, primals_4, primals_5, buf2, buf3, buf4, buf6,
        reinterpret_tensor(buf7, (192, 192, 1, 1), (192, 1, 1, 1), 0), buf8,
        buf10, buf11, buf13, reinterpret_tensor(buf14, (192, 192, 1, 1), (
        192, 1, 1, 1), 0), buf15, buf17, buf18, buf20, reinterpret_tensor(
        buf21, (192, 192, 1, 1), (192, 1, 1, 1), 0), buf22, buf24, buf25,
        buf28, buf29, buf30, buf31, buf32, buf33, buf34, buf35)


class LowerBound(Function):

    @staticmethod
    def forward(ctx, inputs, bound):
        b = torch.ones_like(inputs) * bound
        ctx.save_for_backward(inputs, b)
        return torch.max(inputs, b)

    @staticmethod
    def backward(ctx, grad_output):
        inputs, b = ctx.saved_tensors
        pass_through_1 = inputs >= b
        pass_through_2 = grad_output < 0
        pass_through = pass_through_1 | pass_through_2
        return pass_through.type(grad_output.dtype) * grad_output, None


class GDN(nn.Module):
    """Generalized divisive normalization layer.
    y[i] = x[i] / sqrt(beta[i] + sum_j(gamma[j, i] * x[j]))
    """

    def __init__(self, ch, inverse=False, beta_min=1e-06, gamma_init=0.1,
        reparam_offset=2 ** -18):
        super(GDN, self).__init__()
        self.inverse = inverse
        self.beta_min = beta_min
        self.gamma_init = gamma_init
        self.reparam_offset = reparam_offset
        self.build(ch)

    def build(self, ch):
        self.pedestal = self.reparam_offset ** 2
        self.beta_bound = (self.beta_min + self.reparam_offset ** 2) ** 0.5
        self.gamma_bound = self.reparam_offset
        beta = torch.sqrt(torch.ones(ch) + self.pedestal)
        self.beta = nn.Parameter(beta)
        eye = torch.eye(ch)
        g = self.gamma_init * eye
        g = g + self.pedestal
        gamma = torch.sqrt(g)
        self.gamma = nn.Parameter(gamma)
        self.pedestal = self.pedestal

    def forward(self, inputs):
        unfold = False
        if inputs.dim() == 5:
            unfold = True
            bs, ch, d, w, h = inputs.size()
            inputs = inputs.view(bs, ch, d * w, h)
        _, ch, _, _ = inputs.size()
        beta = LowerBound.apply(self.beta, self.beta_bound)
        beta = beta ** 2 - self.pedestal
        gamma = LowerBound.apply(self.gamma, self.gamma_bound)
        gamma = gamma ** 2 - self.pedestal
        gamma = gamma.view(ch, ch, 1, 1)
        norm_ = nn.functional.conv2d(inputs ** 2, gamma, beta)
        norm_ = torch.sqrt(norm_)
        if self.inverse:
            outputs = inputs * norm_
        else:
            outputs = inputs / norm_
        if unfold:
            outputs = outputs.view(bs, ch, d, w, h)
        return outputs


class expandEncoderNew(nn.Module):
    """
    Analysis net
    """

    def __init__(self, out_channel_N=192):
        super(expandEncoderNew, self).__init__()
        self.conv1 = nn.Conv2d(3, out_channel_N, 9, stride=4, padding=4)
        torch.nn.init.xavier_normal_(self.conv1.weight.data, math.sqrt(2 *
            (3 + out_channel_N) / 6))
        torch.nn.init.constant_(self.conv1.bias.data, 0.01)
        self.gdn1 = GDN(out_channel_N)
        self.conv2 = nn.Conv2d(out_channel_N, out_channel_N, 5, stride=2,
            padding=2)
        torch.nn.init.xavier_normal_(self.conv2.weight.data, math.sqrt(2))
        torch.nn.init.constant_(self.conv2.bias.data, 0.01)
        self.gdn2 = GDN(out_channel_N)
        self.conv3 = nn.Conv2d(out_channel_N, out_channel_N, 3, stride=1,
            padding=1)
        torch.nn.init.xavier_normal_(self.conv3.weight.data, math.sqrt(2))
        torch.nn.init.constant_(self.conv3.bias.data, 0.01)
        self.gdn3 = GDN(out_channel_N)
        self.conv4 = nn.Conv2d(out_channel_N, out_channel_N, 5, stride=2,
            padding=2, bias=False)
        torch.nn.init.xavier_normal_(self.conv4.weight.data, math.sqrt(2))

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.gdn1.beta
        primals_5 = self.gdn1.gamma
        primals_6 = self.conv2.weight
        primals_7 = self.conv2.bias
        primals_8 = self.gdn2.beta
        primals_9 = self.gdn2.gamma
        primals_10 = self.conv3.weight
        primals_11 = self.conv3.bias
        primals_12 = self.gdn3.beta
        primals_13 = self.gdn3.gamma
        primals_14 = self.conv4.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])
        return output[0]


Measured latency_ms: 0.023813