# 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_4/inductor_cache/rl/crlzvualg2za225vkja5fjpnxss76chgi2glfm2hpmsgp2hm7zo3.py
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   y_1 => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%squeeze,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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: '*i1', 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_relu_threshold_backward_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_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 64)
    tmp0 = tl.load(in_out_ptr0 + (x2), 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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3p/c3p33hnyor46am5bbu552bu6hhwxr4bva2unf464y7z76cfgczbh.py
# Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   y_3 => relu_1
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%squeeze_1,), kwargs = {})
triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_relu_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=[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_relu_1', '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_relu_1(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
    x2 = xindex
    x1 = (xindex // 64)
    tmp0 = tl.load(in_out_ptr0 + (x2), 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 + (x2), tmp4, 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, 3), (108, 27, 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, 3), (108, 27, 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(reinterpret_tensor(primals_3, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf7, 256, grid=grid(256), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 4, 4, 4, 4), (0, 64, 16, 4, 1), 0), primals_4, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.relu]
        triton_poi_fused_relu_1.run(buf3, primals_5, 256, grid=grid(256), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [y_4], Original ATen: [aten.max_pool3d_with_indices]
        buf4 = torch.ops.aten.max_pool3d_with_indices.default(buf3, [2, 2, 2], [2, 2, 2], [0, 0, 0], [1, 1, 1], True)
        buf5 = buf4[0]
        buf6 = buf4[1]
        del buf4
    return (buf5, buf3, primals_1, primals_4, reinterpret_tensor(primals_3, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), reinterpret_tensor(buf1, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), buf3, buf6, buf7, )


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, 3), (108, 27, 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, 3), (108, 27, 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 copy
import torch
import torch.nn as nn


def get_conv(dim=3):
    """Chooses an implementation for a convolution layer."""
    if dim == 3:
        return nn.Conv3d
    elif dim == 2:
        return nn.Conv2d
    else:
        raise ValueError('dim has to be 2 or 3')


def planar_kernel(x):
    """Returns a "planar" kernel shape (e.g. for 2D convolution in 3D space)
    that doesn't consider the first spatial dim (D)."""
    if isinstance(x, int):
        return 1, x, x
    else:
        return x


def planar_pad(x):
    """Returns a "planar" padding shape that doesn't pad along the first spatial dim (D)."""
    if isinstance(x, int):
        return 0, x, x
    else:
        return x


def conv3(in_channels, out_channels, kernel_size=3, stride=1, padding=1,
    bias=True, planar=False, dim=3):
    """Returns an appropriate spatial convolution layer, depending on args.
    - dim=2: Conv2d with 3x3 kernel
    - dim=3 and planar=False: Conv3d with 3x3x3 kernel
    - dim=3 and planar=True: Conv3d with 1x3x3 kernel
    """
    if planar:
        stride = planar_kernel(stride)
        padding = planar_pad(padding)
        kernel_size = planar_kernel(kernel_size)
    return get_conv(dim)(in_channels, out_channels, kernel_size=kernel_size,
        stride=stride, padding=padding, bias=bias)


def get_activation(activation):
    if isinstance(activation, str):
        if activation == 'relu':
            return nn.ReLU()
        elif activation == 'leaky':
            return nn.LeakyReLU(negative_slope=0.1)
        elif activation == 'prelu':
            return nn.PReLU(num_parameters=1)
        elif activation == 'rrelu':
            return nn.RReLU()
        elif activation == 'silu':
            return nn.SiLU()
        elif activation == 'lin':
            return nn.Identity()
    else:
        return copy.deepcopy(activation)


def get_maxpool(dim=3):
    """Chooses an implementation for a max-pooling layer."""
    if dim == 3:
        return nn.MaxPool3d
    elif dim == 2:
        return nn.MaxPool2d
    else:
        raise ValueError('dim has to be 2 or 3')


def get_normalization(normtype: 'str', num_channels: 'int', dim: 'int'=3):
    """Chooses an implementation for a batch normalization layer."""
    if normtype is None or normtype == 'none':
        return nn.Identity()
    elif normtype.startswith('group'):
        if normtype == 'group':
            num_groups = 8
        elif len(normtype) > len('group') and normtype[len('group'):].isdigit(
            ):
            num_groups = int(normtype[len('group'):])
        else:
            raise ValueError(
                f'normtype "{normtype}" not understood. It should be "group<G>", where <G> is the number of groups.'
                )
        return nn.GroupNorm(num_groups=num_groups, num_channels=num_channels)
    elif normtype == 'instance':
        if dim == 3:
            return nn.InstanceNorm3d(num_channels)
        elif dim == 2:
            return nn.InstanceNorm2d(num_channels)
        else:
            raise ValueError('dim has to be 2 or 3')
    elif normtype == 'batch':
        if dim == 3:
            return nn.BatchNorm3d(num_channels)
        elif dim == 2:
            return nn.BatchNorm2d(num_channels)
        else:
            raise ValueError('dim has to be 2 or 3')
    else:
        raise ValueError(
            f"""Unknown normalization type "{normtype}".
Valid choices are "batch", "instance", "group" or "group<G>",where <G> is the number of groups."""
            )


class DownConv(nn.Module):
    """
    A helper Module that performs 2 convolutions and 1 MaxPool.
    A ReLU activation follows each convolution.
    """

    def __init__(self, in_channels, out_channels, pooling=True, planar=
        False, activation='relu', normalization=None, full_norm=True, dim=3,
        conv_mode='same'):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.pooling = pooling
        self.normalization = normalization
        self.dim = dim
        padding = 1 if 'same' in conv_mode else 0
        self.conv1 = conv3(self.in_channels, self.out_channels, planar=
            planar, dim=dim, padding=padding)
        self.conv2 = conv3(self.out_channels, self.out_channels, planar=
            planar, dim=dim, padding=padding)
        if self.pooling:
            kernel_size = 2
            if planar:
                kernel_size = planar_kernel(kernel_size)
            self.pool = get_maxpool(dim)(kernel_size=kernel_size, ceil_mode
                =True)
            self.pool_ks = kernel_size
        else:
            self.pool = nn.Identity()
            self.pool_ks = -123
        self.act1 = get_activation(activation)
        self.act2 = get_activation(activation)
        if full_norm:
            self.norm0 = get_normalization(normalization, self.out_channels,
                dim=dim)
        else:
            self.norm0 = nn.Identity()
        self.norm1 = get_normalization(normalization, self.out_channels,
            dim=dim)

    def forward(self, x):
        y = self.conv1(x)
        y = self.norm0(y)
        y = self.act1(y)
        y = self.conv2(y)
        y = self.norm1(y)
        y = self.act2(y)
        before_pool = y
        y = self.pool(y)
        return y, before_pool


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 copy
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 64
    tmp0 = tl.load(in_out_ptr0 + x2, 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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_1(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
    x2 = xindex
    x1 = xindex // 64
    tmp0 = tl.load(in_out_ptr0 + x2, 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 + x2, tmp4, 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, 3), (108, 27, 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, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_3, (1,
            4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(1, 1, 
            1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
            primals_2, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 4, 4,
            4, 4), (0, 64, 16, 4, 1), 0), primals_4, stride=(1, 1, 1),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused_relu_1[grid(256)](buf3, primals_5, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_5
        buf4 = torch.ops.aten.max_pool3d_with_indices.default(buf3, [2, 2, 
            2], [2, 2, 2], [0, 0, 0], [1, 1, 1], True)
        buf5 = buf4[0]
        buf6 = buf4[1]
        del buf4
    return buf5, buf3, primals_1, primals_4, reinterpret_tensor(primals_3,
        (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), reinterpret_tensor(buf1,
        (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), buf3, buf6, buf7


def get_conv(dim=3):
    """Chooses an implementation for a convolution layer."""
    if dim == 3:
        return nn.Conv3d
    elif dim == 2:
        return nn.Conv2d
    else:
        raise ValueError('dim has to be 2 or 3')


def planar_kernel(x):
    """Returns a "planar" kernel shape (e.g. for 2D convolution in 3D space)
    that doesn't consider the first spatial dim (D)."""
    if isinstance(x, int):
        return 1, x, x
    else:
        return x


def planar_pad(x):
    """Returns a "planar" padding shape that doesn't pad along the first spatial dim (D)."""
    if isinstance(x, int):
        return 0, x, x
    else:
        return x


def conv3(in_channels, out_channels, kernel_size=3, stride=1, padding=1,
    bias=True, planar=False, dim=3):
    """Returns an appropriate spatial convolution layer, depending on args.
    - dim=2: Conv2d with 3x3 kernel
    - dim=3 and planar=False: Conv3d with 3x3x3 kernel
    - dim=3 and planar=True: Conv3d with 1x3x3 kernel
    """
    if planar:
        stride = planar_kernel(stride)
        padding = planar_pad(padding)
        kernel_size = planar_kernel(kernel_size)
    return get_conv(dim)(in_channels, out_channels, kernel_size=kernel_size,
        stride=stride, padding=padding, bias=bias)


def get_activation(activation):
    if isinstance(activation, str):
        if activation == 'relu':
            return nn.ReLU()
        elif activation == 'leaky':
            return nn.LeakyReLU(negative_slope=0.1)
        elif activation == 'prelu':
            return nn.PReLU(num_parameters=1)
        elif activation == 'rrelu':
            return nn.RReLU()
        elif activation == 'silu':
            return nn.SiLU()
        elif activation == 'lin':
            return nn.Identity()
    else:
        return copy.deepcopy(activation)


def get_maxpool(dim=3):
    """Chooses an implementation for a max-pooling layer."""
    if dim == 3:
        return nn.MaxPool3d
    elif dim == 2:
        return nn.MaxPool2d
    else:
        raise ValueError('dim has to be 2 or 3')


def get_normalization(normtype: 'str', num_channels: 'int', dim: 'int'=3):
    """Chooses an implementation for a batch normalization layer."""
    if normtype is None or normtype == 'none':
        return nn.Identity()
    elif normtype.startswith('group'):
        if normtype == 'group':
            num_groups = 8
        elif len(normtype) > len('group') and normtype[len('group'):].isdigit(
            ):
            num_groups = int(normtype[len('group'):])
        else:
            raise ValueError(
                f'normtype "{normtype}" not understood. It should be "group<G>", where <G> is the number of groups.'
                )
        return nn.GroupNorm(num_groups=num_groups, num_channels=num_channels)
    elif normtype == 'instance':
        if dim == 3:
            return nn.InstanceNorm3d(num_channels)
        elif dim == 2:
            return nn.InstanceNorm2d(num_channels)
        else:
            raise ValueError('dim has to be 2 or 3')
    elif normtype == 'batch':
        if dim == 3:
            return nn.BatchNorm3d(num_channels)
        elif dim == 2:
            return nn.BatchNorm2d(num_channels)
        else:
            raise ValueError('dim has to be 2 or 3')
    else:
        raise ValueError(
            f"""Unknown normalization type "{normtype}".
Valid choices are "batch", "instance", "group" or "group<G>",where <G> is the number of groups."""
            )


class DownConvNew(nn.Module):
    """
    A helper Module that performs 2 convolutions and 1 MaxPool.
    A ReLU activation follows each convolution.
    """

    def __init__(self, in_channels, out_channels, pooling=True, planar=
        False, activation='relu', normalization=None, full_norm=True, dim=3,
        conv_mode='same'):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.pooling = pooling
        self.normalization = normalization
        self.dim = dim
        padding = 1 if 'same' in conv_mode else 0
        self.conv1 = conv3(self.in_channels, self.out_channels, planar=
            planar, dim=dim, padding=padding)
        self.conv2 = conv3(self.out_channels, self.out_channels, planar=
            planar, dim=dim, padding=padding)
        if self.pooling:
            kernel_size = 2
            if planar:
                kernel_size = planar_kernel(kernel_size)
            self.pool = get_maxpool(dim)(kernel_size=kernel_size, ceil_mode
                =True)
            self.pool_ks = kernel_size
        else:
            self.pool = nn.Identity()
            self.pool_ks = -123
        self.act1 = get_activation(activation)
        self.act2 = get_activation(activation)
        if full_norm:
            self.norm0 = get_normalization(normalization, self.out_channels,
                dim=dim)
        else:
            self.norm0 = nn.Identity()
        self.norm1 = get_normalization(normalization, self.out_channels,
            dim=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]
