# 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/lc/clcz6su3ieedndcvucruacshqmnwkfpvhq36lchxjwinoruby6or.py
# Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   pad => constant_pad_nd
# Graph fragment:
#   %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [0, 1, 0, 0], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = (xindex // 5)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.load(in_ptr0 + (x0 + (4*x1)), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + (x2), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/5c/c5ctxd6oripki6d44w5jxpdopl34hh2qcvk44v2k4462w7l7ikkg.py
# Topologically Sorted Source Nodes: [pad_1], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   pad_1 => constant_pad_nd_1
# Graph fragment:
#   %constant_pad_nd_1 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [0, 0, 0, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_1 = async_compile.triton('triton_poi_fused_constant_pad_nd_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_constant_pad_nd_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 5
    x2 = (xindex // 20)
    x3 = xindex % 20
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.load(in_ptr0 + (x3 + (16*x2)), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + (x4), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/2c/c2ctg3dslwa656ircovg33gdpvmm6egep4iaifa6h5fgd5y4fvvc.py
# Topologically Sorted Source Nodes: [setitem], Original ATen: [aten.lift_fresh, aten.fill]
# Source node to ATen node mapping:
#   setitem => copy, full_default
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select, %full_default), kwargs = {})
#   %select_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%convolution, %copy, 3, 3), kwargs = {})
triton_poi_fused_fill_lift_fresh_2 = async_compile.triton('triton_poi_fused_fill_lift_fresh_2', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_fill_lift_fresh_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_fill_lift_fresh_2(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp0 = x0
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tl.store(in_out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/su/csuiwmogyq4obylbzxzzeheqczv7hr636aujxyzailytjstqhq3q.py
# Topologically Sorted Source Nodes: [setitem, setitem_1], Original ATen: [aten.lift_fresh, aten.fill]
# Source node to ATen node mapping:
#   setitem => full_default
#   setitem_1 => copy_1
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %copy_1 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_3, %full_default), kwargs = {})
#   %select_scatter_default_1 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%convolution_1, %copy_1, 2, 3), kwargs = {})
triton_poi_fused_fill_lift_fresh_3 = async_compile.triton('triton_poi_fused_fill_lift_fresh_3', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_fill_lift_fresh_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_fill_lift_fresh_3(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 4
    x3 = xindex
    tmp3 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tl.store(in_out_ptr0 + (x3), tmp5, 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, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (1, 1, 1, 2), (2, 2, 2, 1))
    assert_size_stride(primals_3, (1, 1, 2, 1), (2, 2, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 5), (20, 20, 5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 80, grid=grid(80), stream=stream0)
        buf1 = empty_strided_cuda((4, 1, 5, 4), (20, 20, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pad_1], Original ATen: [aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_1.run(primals_1, buf1, 80, grid=grid(80), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [diff_x], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 4, 4), (16, 16, 4, 1))
        # Topologically Sorted Source Nodes: [diff_y], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
        buf4 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [setitem], Original ATen: [aten.lift_fresh, aten.fill]
        triton_poi_fused_fill_lift_fresh_2.run(buf4, 64, grid=grid(64), stream=stream0)
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [setitem, setitem_1], Original ATen: [aten.lift_fresh, aten.fill]
        triton_poi_fused_fill_lift_fresh_3.run(buf5, 64, grid=grid(64), stream=stream0)
    return (buf4, buf5, buf1, buf0, primals_2, primals_3, buf0, buf1, )


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, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 1, 1, 2), (2, 2, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 1, 2, 1), (2, 2, 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
import torch.nn.functional as F


class Forward_Grad(nn.Module):

    def __init__(self):
        super(Forward_Grad, self).__init__()
        self.x_ker_init = torch.tensor([[[[-1, 1]]]], dtype=torch.float,
            requires_grad=True)
        self.y_ker_init = torch.tensor([[[[-1], [1]]]], dtype=torch.float,
            requires_grad=True)
        self.forward_conv_x = nn.Conv2d(1, 1, (1, 2), bias=False)
        self.forward_conv_x.weight.data = self.x_ker_init
        self.forward_conv_y = nn.Conv2d(1, 1, (2, 1), bias=False)
        self.forward_conv_y.weight.data = self.y_ker_init

    def forward(self, x):
        assert len(x.shape) == 4
        x.padright = F.pad(x, (0, 1, 0, 0))
        x.padbottom = F.pad(x, (0, 0, 0, 1))
        diff_x = self.forward_conv_x(x.padright)
        diff_y = self.forward_conv_y(x.padbottom)
        diff_x[:, :, :, x.shape[3] - 1] = 0
        diff_y[:, :, x.shape[2] - 1, :] = 0
        return diff_x, diff_y


def get_inputs():
    return [torch.rand([4, 1, 4, 4])]


def get_init_inputs():
    return [[], {}]

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 torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 5
    x2 = xindex // 20
    x3 = xindex % 20
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.load(in_ptr0 + (x3 + 16 * x2), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + x4, tmp3, xmask)


@triton.jit
def triton_poi_fused_fill_lift_fresh_2(in_out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_out_ptr0 + x2, xmask)
    tmp0 = x0
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tl.store(in_out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_fill_lift_fresh_3(in_out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x3 = xindex
    tmp3 = tl.load(in_out_ptr0 + x3, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tl.store(in_out_ptr0 + x3, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (1, 1, 1, 2), (2, 2, 2, 1))
    assert_size_stride(primals_3, (1, 1, 2, 1), (2, 2, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 5), (20, 20, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(80)](primals_1, buf0, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 1, 5, 4), (20, 20, 4, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_1[grid(80)](primals_1, buf1, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf2 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 4, 4), (16, 16, 4, 1))
        buf3 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
        buf4 = buf2
        del buf2
        triton_poi_fused_fill_lift_fresh_2[grid(64)](buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = buf3
        del buf3
        triton_poi_fused_fill_lift_fresh_3[grid(64)](buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
    return buf4, buf5, buf1, buf0, primals_2, primals_3, buf0, buf1


class Forward_GradNew(nn.Module):

    def __init__(self):
        super(Forward_GradNew, self).__init__()
        self.x_ker_init = torch.tensor([[[[-1, 1]]]], dtype=torch.float,
            requires_grad=True)
        self.y_ker_init = torch.tensor([[[[-1], [1]]]], dtype=torch.float,
            requires_grad=True)
        self.forward_conv_x = nn.Conv2d(1, 1, (1, 2), bias=False)
        self.forward_conv_x.weight.data = self.x_ker_init
        self.forward_conv_y = nn.Conv2d(1, 1, (2, 1), bias=False)
        self.forward_conv_y.weight.data = self.y_ker_init

    def forward(self, input_0):
        primals_2 = self.forward_conv_x.weight
        primals_3 = self.forward_conv_y.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1]
