# 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/td/ctdybbibnws4d7ukbk3fpn35zkgapxylowdhzwx7vgsllncbdrxa.py
# Topologically Sorted Source Nodes: [y, y_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   y => convolution
#   y_1 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_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=[256], 
    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_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, 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, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/it/citiw25o2bzpowghghyesgdfdm2lebsqwdmo6qg6foemy373lm2q.py
# Topologically Sorted Source Nodes: [y_4], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   y_4 => getitem, getitem_1
# Graph fragment:
#   %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = (xindex // 2)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (8*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (8*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + (2*x0) + (8*x1)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + (2*x0) + (8*x1)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
    tl.store(out_ptr1 + (x2), tmp16, 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, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.convolution]
        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, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [y, y_1], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 256, grid=grid(256), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.convolution]
        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, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [y_2, y_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 256, grid=grid(256), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.int8)
        # Topologically Sorted Source Nodes: [y_4], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf3, buf4, buf5, 64, grid=grid(64), stream=stream0)
    return (buf4, buf3, primals_1, primals_3, primals_4, buf1, buf3, buf5, )


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, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (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


def get_activation(activation: 'str'):
    if activation == 'relu':
        return nn.ReLU()
    elif activation == 'leaky':
        return nn.LeakyReLU(negative_slope=0.1)
    elif activation == 'elu':
        return nn.ELU()


def conv_layer(dim: 'int'):
    if dim == 3:
        return nn.Conv3d
    elif dim == 2:
        return nn.Conv2d


def get_conv_layer(in_channels: 'int', out_channels: 'int', kernel_size:
    'int'=3, stride: 'int'=1, padding: 'int'=1, bias: 'bool'=True, dim: 'int'=2
    ):
    return conv_layer(dim)(in_channels, out_channels, kernel_size=
        kernel_size, stride=stride, padding=padding, bias=bias)


def maxpool_layer(dim: 'int'):
    if dim == 3:
        return nn.MaxPool3d
    elif dim == 2:
        return nn.MaxPool2d


def get_maxpool_layer(kernel_size: 'int'=2, stride: 'int'=2, padding: 'int'
    =0, dim: 'int'=2):
    return maxpool_layer(dim=dim)(kernel_size=kernel_size, stride=stride,
        padding=padding)


def get_normalization(normalization: 'str', num_channels: 'int', dim: 'int'):
    if normalization == 'batch':
        if dim == 3:
            return nn.BatchNorm3d(num_channels)
        elif dim == 2:
            return nn.BatchNorm2d(num_channels)
    elif normalization == 'instance':
        if dim == 3:
            return nn.InstanceNorm3d(num_channels)
        elif dim == 2:
            return nn.InstanceNorm2d(num_channels)
    elif 'group' in normalization:
        num_groups = int(normalization.partition('group')[-1])
        return nn.GroupNorm(num_groups=num_groups, num_channels=num_channels)


class DownBlock(nn.Module):
    """
    A helper Module that performs 2 Convolutions and 1 MaxPool.
    An activation follows each convolution.
    A normalization layer follows each convolution.
    """

    def __init__(self, in_channels: 'int', out_channels: 'int', pooling:
        'bool'=True, activation: 'str'='relu', normalization: 'str'=None,
        dim: 'str'=2, conv_mode: 'str'='same'):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.pooling = pooling
        self.normalization = normalization
        if conv_mode == 'same':
            self.padding = 1
        elif conv_mode == 'valid':
            self.padding = 0
        self.dim = dim
        self.activation = activation
        self.conv1 = get_conv_layer(self.in_channels, self.out_channels,
            kernel_size=3, stride=1, padding=self.padding, bias=True, dim=
            self.dim)
        self.conv2 = get_conv_layer(self.out_channels, self.out_channels,
            kernel_size=3, stride=1, padding=self.padding, bias=True, dim=
            self.dim)
        if self.pooling:
            self.pool = get_maxpool_layer(kernel_size=2, stride=2, padding=
                0, dim=self.dim)
        self.act1 = get_activation(self.activation)
        self.act2 = get_activation(self.activation)
        if self.normalization:
            self.norm1 = get_normalization(normalization=self.normalization,
                num_channels=self.out_channels, dim=self.dim)
            self.norm2 = get_normalization(normalization=self.normalization,
                num_channels=self.out_channels, dim=self.dim)

    def forward(self, x):
        y = self.conv1(x)
        y = self.act1(y)
        if self.normalization:
            y = self.norm1(y)
        y = self.conv2(y)
        y = self.act2(y)
        if self.normalization:
            y = self.norm2(y)
        before_pooling = y
        if self.pooling:
            y = self.pool(y)
        return y, before_pooling


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_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
from torch._inductor.runtime import triton_helpers
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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp16, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (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, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(256)](buf1, primals_2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        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, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_0[grid(256)](buf3, primals_5, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(64)](buf3, buf4,
            buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return buf4, buf3, primals_1, primals_3, primals_4, buf1, buf3, buf5


def get_activation(activation: 'str'):
    if activation == 'relu':
        return nn.ReLU()
    elif activation == 'leaky':
        return nn.LeakyReLU(negative_slope=0.1)
    elif activation == 'elu':
        return nn.ELU()


def conv_layer(dim: 'int'):
    if dim == 3:
        return nn.Conv3d
    elif dim == 2:
        return nn.Conv2d


def get_conv_layer(in_channels: 'int', out_channels: 'int', kernel_size:
    'int'=3, stride: 'int'=1, padding: 'int'=1, bias: 'bool'=True, dim: 'int'=2
    ):
    return conv_layer(dim)(in_channels, out_channels, kernel_size=
        kernel_size, stride=stride, padding=padding, bias=bias)


def maxpool_layer(dim: 'int'):
    if dim == 3:
        return nn.MaxPool3d
    elif dim == 2:
        return nn.MaxPool2d


def get_maxpool_layer(kernel_size: 'int'=2, stride: 'int'=2, padding: 'int'
    =0, dim: 'int'=2):
    return maxpool_layer(dim=dim)(kernel_size=kernel_size, stride=stride,
        padding=padding)


def get_normalization(normalization: 'str', num_channels: 'int', dim: 'int'):
    if normalization == 'batch':
        if dim == 3:
            return nn.BatchNorm3d(num_channels)
        elif dim == 2:
            return nn.BatchNorm2d(num_channels)
    elif normalization == 'instance':
        if dim == 3:
            return nn.InstanceNorm3d(num_channels)
        elif dim == 2:
            return nn.InstanceNorm2d(num_channels)
    elif 'group' in normalization:
        num_groups = int(normalization.partition('group')[-1])
        return nn.GroupNorm(num_groups=num_groups, num_channels=num_channels)


class DownBlockNew(nn.Module):
    """
    A helper Module that performs 2 Convolutions and 1 MaxPool.
    An activation follows each convolution.
    A normalization layer follows each convolution.
    """

    def __init__(self, in_channels: 'int', out_channels: 'int', pooling:
        'bool'=True, activation: 'str'='relu', normalization: 'str'=None,
        dim: 'str'=2, conv_mode: 'str'='same'):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.pooling = pooling
        self.normalization = normalization
        if conv_mode == 'same':
            self.padding = 1
        elif conv_mode == 'valid':
            self.padding = 0
        self.dim = dim
        self.activation = activation
        self.conv1 = get_conv_layer(self.in_channels, self.out_channels,
            kernel_size=3, stride=1, padding=self.padding, bias=True, dim=
            self.dim)
        self.conv2 = get_conv_layer(self.out_channels, self.out_channels,
            kernel_size=3, stride=1, padding=self.padding, bias=True, dim=
            self.dim)
        if self.pooling:
            self.pool = get_maxpool_layer(kernel_size=2, stride=2, padding=
                0, dim=self.dim)
        self.act1 = get_activation(self.activation)
        self.act2 = get_activation(self.activation)
        if self.normalization:
            self.norm1 = get_normalization(normalization=self.normalization,
                num_channels=self.out_channels, dim=self.dim)
            self.norm2 = get_normalization(normalization=self.normalization,
                num_channels=self.out_channels, dim=self.dim)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]
