# 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_6/inductor_cache/it/cittqitccw6vgbgjm3dqut4jy5pep47qoosplnw4qp55jzbbllk7.py
# Topologically Sorted Source Nodes: [conv2d, conv2d_1, conv2d_2, conv2d_3], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
#   conv2d_1 => convolution_1
#   conv2d_2 => convolution_2
#   conv2d_3 => convolution_3
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%arg1_1, %arg0_1, None, [2, 2], [0, 0], [1, 1], False, [0, 0], 4), kwargs = {})
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%arg1_1, %arg2_1, None, [2, 2], [0, 0], [1, 1], False, [0, 0], 4), kwargs = {})
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%arg1_1, %arg3_1, None, [2, 2], [0, 0], [1, 1], False, [0, 0], 4), kwargs = {})
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%arg1_1, %arg4_1, None, [2, 2], [0, 0], [1, 1], False, [0, 0], 4), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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, 16], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': [], '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_convolution_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2, out_ptr3, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    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
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask)
    tl.store(out_ptr0 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
    tl.store(out_ptr2 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
    tl.store(out_ptr3 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/5i/c5iuv6v7eggudqpwvokzc6nbwpfull4j36zkwmtjfwwtnhluvvhi.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%arg1_1, %arg0_1, None, [2, 2], [0, 0], [1, 1], False, [0, 0], 4), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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, 4], tile_hint=TileHint.SQUARE,
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': [], '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_convolution_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    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 % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(arg3_1, (4, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(arg4_1, (4, 1, 2, 2), (4, 4, 2, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, conv2d_1, conv2d_2, conv2d_3], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(arg1_1, buf0, buf3, buf6, buf9, 16, 16, grid=grid(16, 16), stream=stream0)
        del arg1_1
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, arg0_1, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf1, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg0_1
        del buf0
        buf2 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf1, buf2, 16, 4, grid=grid(16, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, arg2_1, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf4, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg2_1
        del buf3
        buf5 = reinterpret_tensor(buf1, (4, 4, 2, 2), (16, 4, 2, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf4, buf5, 16, 4, grid=grid(16, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf6, arg3_1, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf7, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg3_1
        del buf6
        buf8 = reinterpret_tensor(buf4, (4, 4, 2, 2), (16, 4, 2, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf7, buf8, 16, 4, grid=grid(16, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, arg4_1, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf10, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg4_1
        del buf9
        buf11 = reinterpret_tensor(buf7, (4, 4, 2, 2), (16, 4, 2, 1), 0); del buf7  # reuse
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf10, buf11, 16, 4, grid=grid(16, 4), stream=stream0)
        del buf10
    return (buf2, buf5, buf8, buf11, )


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, 1, 2, 2), (4, 4, 2, 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)
    arg2_1 = rand_strided((4, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    arg4_1 = rand_strided((4, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_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 numpy as np
from torch import nn


def getWavelet(in_channels, pool=True):
    """wavelet decomposition using conv2d"""
    harr_wav_L = 1 / np.sqrt(2) * np.ones((1, 2))
    harr_wav_H = 1 / np.sqrt(2) * np.ones((1, 2))
    harr_wav_H[0, 0] = -1 * harr_wav_H[0, 0]
    harr_wav_LL = np.transpose(harr_wav_L) * harr_wav_L
    harr_wav_LH = np.transpose(harr_wav_L) * harr_wav_H
    harr_wav_HL = np.transpose(harr_wav_H) * harr_wav_L
    harr_wav_HH = np.transpose(harr_wav_H) * harr_wav_H
    filter_LL = torch.from_numpy(harr_wav_LL).unsqueeze(0)
    filter_LH = torch.from_numpy(harr_wav_LH).unsqueeze(0)
    filter_HL = torch.from_numpy(harr_wav_HL).unsqueeze(0)
    filter_HH = torch.from_numpy(harr_wav_HH).unsqueeze(0)
    if pool:
        net = nn.Conv2d
    else:
        net = nn.ConvTranspose2d
    LL = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    LH = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    HL = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    HH = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    LL.weight.requires_grad = False
    LH.weight.requires_grad = False
    HL.weight.requires_grad = False
    HH.weight.requires_grad = False
    LL.weight.data = filter_LL.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    LH.weight.data = filter_LH.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    HL.weight.data = filter_HL.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    HH.weight.data = filter_HH.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    return LL, LH, HL, HH


class WavePool(nn.Module):

    def __init__(self, in_channels):
        super(WavePool, self).__init__()
        self.LL, self.LH, self.HL, self.HH = getWavelet(in_channels)

    def forward(self, x):
        return self.LL(x), self.LH(x), self.HL(x), self.HH(x)


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


def get_init_inputs():
    return [[], {'in_channels': 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
import numpy as np
from torch import 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_convolution_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    out_ptr3, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
    tl.store(out_ptr2 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
    tl.store(out_ptr3 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    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 % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(arg3_1, (4, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(arg4_1, (4, 1, 2, 2), (4, 4, 2, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 16)](arg1_1, buf0, buf3,
            buf6, buf9, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1
            )
        del arg1_1
        buf1 = extern_kernels.convolution(buf0, arg0_1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf1, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg0_1
        del buf0
        buf2 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_convolution_1[grid(16, 4)](buf1, buf2, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, arg2_1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf4, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg2_1
        del buf3
        buf5 = reinterpret_tensor(buf1, (4, 4, 2, 2), (16, 4, 2, 1), 0)
        del buf1
        triton_poi_fused_convolution_1[grid(16, 4)](buf4, buf5, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf7 = extern_kernels.convolution(buf6, arg3_1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf7, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg3_1
        del buf6
        buf8 = reinterpret_tensor(buf4, (4, 4, 2, 2), (16, 4, 2, 1), 0)
        del buf4
        triton_poi_fused_convolution_1[grid(16, 4)](buf7, buf8, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf10 = extern_kernels.convolution(buf9, arg4_1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf10, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg4_1
        del buf9
        buf11 = reinterpret_tensor(buf7, (4, 4, 2, 2), (16, 4, 2, 1), 0)
        del buf7
        triton_poi_fused_convolution_1[grid(16, 4)](buf10, buf11, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        del buf10
    return buf2, buf5, buf8, buf11


def getWavelet(in_channels, pool=True):
    """wavelet decomposition using conv2d"""
    harr_wav_L = 1 / np.sqrt(2) * np.ones((1, 2))
    harr_wav_H = 1 / np.sqrt(2) * np.ones((1, 2))
    harr_wav_H[0, 0] = -1 * harr_wav_H[0, 0]
    harr_wav_LL = np.transpose(harr_wav_L) * harr_wav_L
    harr_wav_LH = np.transpose(harr_wav_L) * harr_wav_H
    harr_wav_HL = np.transpose(harr_wav_H) * harr_wav_L
    harr_wav_HH = np.transpose(harr_wav_H) * harr_wav_H
    filter_LL = torch.from_numpy(harr_wav_LL).unsqueeze(0)
    filter_LH = torch.from_numpy(harr_wav_LH).unsqueeze(0)
    filter_HL = torch.from_numpy(harr_wav_HL).unsqueeze(0)
    filter_HH = torch.from_numpy(harr_wav_HH).unsqueeze(0)
    if pool:
        net = nn.Conv2d
    else:
        net = nn.ConvTranspose2d
    LL = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    LH = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    HL = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    HH = net(in_channels, in_channels, kernel_size=2, stride=2, padding=0,
        bias=False, groups=in_channels)
    LL.weight.requires_grad = False
    LH.weight.requires_grad = False
    HL.weight.requires_grad = False
    HH.weight.requires_grad = False
    LL.weight.data = filter_LL.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    LH.weight.data = filter_LH.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    HL.weight.data = filter_HL.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    HH.weight.data = filter_HH.float().unsqueeze(0).expand(in_channels, -1,
        -1, -1)
    return LL, LH, HL, HH


class WavePoolNew(nn.Module):

    def __init__(self, in_channels):
        super(WavePoolNew, self).__init__()
        self.LL, self.LH, self.HL, self.HH = getWavelet(in_channels)

    def forward(self, input_0):
        arg0_1 = self.LL.weight
        arg2_1 = self.LH.weight
        arg3_1 = self.HL.weight
        arg4_1 = self.HH.weight
        arg1_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1])
        return output[0], output[1], output[2], output[3]
