# 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_8/inductor_cache/st/cstyisijjjcom4h3fmnm5l3jk6rnlzjwhirorf3vpbk7do6htdzs.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, [2, 2, 2, 2], 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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_constant_pad_nd_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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 8) % 8
    x0 = xindex % 8
    x2 = (xindex // 64)
    x4 = xindex
    tmp0 = (-2) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-2) + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-10) + x0 + (4*x1) + (16*x2)), tmp10 & xmask, other=0.0)
    tl.store(out_ptr0 + (x4), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/th/cth2ocuys7pc7ywjntvhwtk6ot2bldkofkbhkq4mun25cfscmwm4.py
# Topologically Sorted Source Nodes: [x, group_norm, x_2], Original ATen: [aten.convolution, aten.native_group_norm, aten.pixel_shuffle]
# Source node to ATen node mapping:
#   group_norm => add, add_1, mul_1, rsqrt, var_mean
#   x => convolution
#   x_2 => clone
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %unsqueeze_5), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %unsqueeze_2), kwargs = {})
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
triton_per_fused_convolution_native_group_norm_pixel_shuffle_1 = async_compile.triton('triton_per_fused_convolution_native_group_norm_pixel_shuffle_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.persistent_reduction(
    size_hints=[64, 32],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: '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, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_convolution_native_group_norm_pixel_shuffle_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 4, '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_convolution_native_group_norm_pixel_shuffle_1(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 25
    RBLOCK: tl.constexpr = 32
    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 = rindex < rnumel
    r2 = rindex
    x3 = xindex
    x0 = xindex % 16
    r7 = rindex % 5
    r8 = (rindex // 5)
    x4 = xindex % 2
    x5 = (xindex // 2) % 2
    x6 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (r2 + (25*x3)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(rmask & xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(rmask & xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 25, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(rmask & xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 25.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = 0.0
    tmp31 = tmp29 > tmp30
    tmp32 = 1.0
    tmp33 = tmp29 * tmp32
    tmp34 = libdevice.expm1(tmp33)
    tmp35 = tmp34 * tmp32
    tmp36 = tl.where(tmp31, tmp33, tmp35)
    tl.store(in_out_ptr0 + (r2 + (25*x3)), tmp2, rmask & xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp23, xmask)
    tl.store(out_ptr2 + (x4 + (2*r7) + (10*x5) + (20*r8) + (100*x6)), tmp36, rmask & xmask)
    tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (16, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (16, ), (1, ))
    assert_size_stride(primals_4, (16, ), (1, ))
    assert_size_stride(primals_5, (16, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 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, 1024, grid=grid(1024), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf1 = 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(buf1, (4, 16, 5, 5), (400, 25, 5, 1))
        buf2 = buf1; del buf1  # reuse
        buf3 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 64, 64), torch.float32)
        buf6 = reinterpret_tensor(buf4, (4, 16, 1, 1), (16, 1, 1, 1), 0); del buf4  # reuse
        buf8 = empty_strided_cuda((4, 4, 5, 2, 5, 2), (400, 100, 20, 10, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, group_norm, x_2], Original ATen: [aten.convolution, aten.native_group_norm, aten.pixel_shuffle]
        triton_per_fused_convolution_native_group_norm_pixel_shuffle_1.run(buf2, buf6, primals_3, primals_4, primals_5, buf3, buf8, 64, 25, grid=grid(64), stream=stream0)
        del primals_3
    return (reinterpret_tensor(buf8, (4, 4, 10, 10), (400, 100, 10, 1), 0), primals_2, primals_4, primals_5, buf0, buf2, buf3, 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((16, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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


class Conv2D(nn.Module):
    """
    2D convolution with GroupNorm and ELU

    Parameters
    ----------
    in_channels : int
        Number of input channels
    out_channels : int
        Number of output channels
    kernel_size : int
        Kernel size
    stride : int
        Stride
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride):
        super().__init__()
        self.kernel_size = kernel_size
        self.conv_base = nn.Conv2d(in_channels, out_channels, kernel_size=
            kernel_size, stride=stride)
        self.pad = nn.ConstantPad2d([kernel_size // 2] * 4, value=0)
        self.normalize = torch.nn.GroupNorm(16, out_channels)
        self.activ = nn.ELU(inplace=True)

    def forward(self, x):
        """Runs the Conv2D layer."""
        x = self.conv_base(self.pad(x))
        return self.activ(self.normalize(x))


class UnpackLayerConv2d(nn.Module):
    """
    Unpacking layer with 2d convolutions. Takes a [B,C,H,W] tensor, convolves it
    to produce [B,(r^2)C,H,W] and then unpacks it to produce [B,C,rH,rW].
    """

    def __init__(self, in_channels, out_channels, kernel_size, r=2):
        """
        Initializes a UnpackLayerConv2d object.

        Parameters
        ----------
        in_channels : int
            Number of input channels
        out_channels : int
            Number of output channels
        kernel_size : int
            Kernel size
        r : int
            Packing ratio
        """
        super().__init__()
        self.conv = Conv2D(in_channels, out_channels * r ** 2, kernel_size, 1)
        self.unpack = nn.PixelShuffle(r)

    def forward(self, x):
        """Runs the UnpackLayerConv2d layer."""
        x = self.conv(x)
        x = self.unpack(x)
        return x


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 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_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 8 % 8
    x0 = xindex % 8
    x2 = xindex // 64
    x4 = xindex
    tmp0 = -2 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -2 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-10 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x4, tmp11, xmask)


@triton.jit
def triton_per_fused_convolution_native_group_norm_pixel_shuffle_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    rnumel = 25
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r2 = rindex
    x3 = xindex
    x0 = xindex % 16
    r7 = rindex % 5
    r8 = rindex // 5
    x4 = xindex % 2
    x5 = xindex // 2 % 2
    x6 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + 25 * x3), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tl.where(rmask & xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(rmask & xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 25, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(rmask & xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 25.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tmp30 = 0.0
    tmp31 = tmp29 > tmp30
    tmp32 = 1.0
    tmp33 = tmp29 * tmp32
    tmp34 = libdevice.expm1(tmp33)
    tmp35 = tmp34 * tmp32
    tmp36 = tl.where(tmp31, tmp33, tmp35)
    tl.store(in_out_ptr0 + (r2 + 25 * x3), tmp2, rmask & xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp23, xmask)
    tl.store(out_ptr2 + (x4 + 2 * r7 + 10 * x5 + 20 * r8 + 100 * x6), tmp36,
        rmask & xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (16, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (16,), (1,))
    assert_size_stride(primals_4, (16,), (1,))
    assert_size_stride(primals_5, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(1024)](primals_1, buf0, 
            1024, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = 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(buf1, (4, 16, 5, 5), (400, 25, 5, 1))
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 64, 64), torch.float32
            )
        buf6 = reinterpret_tensor(buf4, (4, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf4
        buf8 = empty_strided_cuda((4, 4, 5, 2, 5, 2), (400, 100, 20, 10, 2,
            1), torch.float32)
        triton_per_fused_convolution_native_group_norm_pixel_shuffle_1[grid(64)
            ](buf2, buf6, primals_3, primals_4, primals_5, buf3, buf8, 64, 
            25, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf8, (4, 4, 10, 10), (400, 100, 10, 1), 0
        ), primals_2, primals_4, primals_5, buf0, buf2, buf3, buf6


class Conv2D(nn.Module):
    """
    2D convolution with GroupNorm and ELU

    Parameters
    ----------
    in_channels : int
        Number of input channels
    out_channels : int
        Number of output channels
    kernel_size : int
        Kernel size
    stride : int
        Stride
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride):
        super().__init__()
        self.kernel_size = kernel_size
        self.conv_base = nn.Conv2d(in_channels, out_channels, kernel_size=
            kernel_size, stride=stride)
        self.pad = nn.ConstantPad2d([kernel_size // 2] * 4, value=0)
        self.normalize = torch.nn.GroupNorm(16, out_channels)
        self.activ = nn.ELU(inplace=True)

    def forward(self, x):
        """Runs the Conv2D layer."""
        x = self.conv_base(self.pad(x))
        return self.activ(self.normalize(x))


class UnpackLayerConv2dNew(nn.Module):
    """
    Unpacking layer with 2d convolutions. Takes a [B,C,H,W] tensor, convolves it
    to produce [B,(r^2)C,H,W] and then unpacks it to produce [B,C,rH,rW].
    """

    def __init__(self, in_channels, out_channels, kernel_size, r=2):
        """
        Initializes a UnpackLayerConv2d object.

        Parameters
        ----------
        in_channels : int
            Number of input channels
        out_channels : int
            Number of output channels
        kernel_size : int
            Kernel size
        r : int
            Packing ratio
        """
        super().__init__()
        self.conv = Conv2D(in_channels, out_channels * r ** 2, kernel_size, 1)
        self.unpack = nn.PixelShuffle(r)

    def forward(self, input_0):
        primals_2 = self.conv.conv_base.weight
        primals_3 = self.conv.conv_base.bias
        primals_4 = self.conv.normalize.weight
        primals_5 = self.conv.normalize.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
