code: def __init__(
      self,
      block: Type[Union[BasicBlock, Bottleneck]],
      layers: List[int],
      zero_init_residual: bool = False,
      groups: int = 1,
      widen: int = 1,
      width_per_group: int = 64,
      replace_stride_with_dilation: Optional[List[bool]] = None,
      norm_layer: Optional[Callable[..., nn.Module]] = None,
      first_conv3x3: bool = False,
      remove_first_maxpool: bool = False,
      in_chans: int = 3,
  ) -> None:
      super().__init__()
  
      if norm_layer is None:
          norm_layer = nn.BatchNorm2d
      self._norm_layer = norm_layer
  
      self.inplanes = width_per_group * widen
      self.dilation = 1
      if replace_stride_with_dilation is None:
          # each element in the tuple indicates if we should replace
          # the 2x2 stride with a dilated convolution instead
          replace_stride_with_dilation = [False, False, False]
      if len(replace_stride_with_dilation) != 3:
          raise ValueError(
              "replace_stride_with_dilation should be None "
              "or a 3-element tuple, got {}".format(
                  replace_stride_with_dilation
              )
          )
      self.groups = groups
      self.base_width = width_per_group
  
      num_out_filters = width_per_group * widen
  
      if first_conv3x3:
          self.conv1 = nn.Conv2d(
              in_chans,
              num_out_filters,
              kernel_size=3,
              stride=1,
              padding=1,
              bias=False,
          )
      else:
          self.conv1 = nn.Conv2d(
              in_chans,
              num_out_filters,
              kernel_size=7,
              stride=2,
              padding=3,
              bias=False,
          )
  
      self.bn1 = norm_layer(num_out_filters)
      self.relu = nn.ReLU(inplace=True)
  
      if remove_first_maxpool:
          self.maxpool = nn.MaxPool2d(kernel_size=1, stride=1)
      else:
          self.maxpool = nn.MaxPool2d(
              kernel_size=3, stride=2, padding=1
          )
  
      self.layer1 = self._make_layer(block, num_out_filters, layers[0])
      num_out_filters *= 2
      self.layer2 = self._make_layer(
          block,
          num_out_filters,
          layers[1],
          stride=2,
          dilate=replace_stride_with_dilation[0],
      )
      num_out_filters *= 2
      self.layer3 = self._make_layer(
          block,
          num_out_filters,
          layers[2],
          stride=2,
          dilate=replace_stride_with_dilation[1],
      )
      num_out_filters *= 2
      self.layer4 = self._make_layer(
          block,
          num_out_filters,
          layers[3],
          stride=2,
          dilate=replace_stride_with_dilation[2],
      )
      self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
  
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              nn.init.kaiming_normal_(
                  m.weight, mode="fan_out", nonlinearity="relu"
              )
          elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
              nn.init.constant_(m.weight, 1)
              nn.init.constant_(m.bias, 0)
  
      # Zero-initialize the last BN in each residual branch,
      # so that the residual branch starts with zeros, and each residual block behaves like an identity.
      # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
      if zero_init_residual:
          for m in self.modules():
              if isinstance(m, Bottleneck):
                  nn.init.constant_(m.bn3.weight, 0)
              elif isinstance(m, BasicBlock):
                  nn.init.constant_(m.bn2.weight, 0)
  
imports: from functools import partial
  from typing import Any, Callable, List, Optional, Type, Union
  import torch
  import torch.nn
  from torch import Tensor
  from torch.hub import load_state_dict_from_url
  from flash.core.registry import FlashRegistry
  from flash.core.utilities.imports import _TIMM_AVAILABLE
  from flash.core.utilities.url_error import catch_url_error
label_desc: Use image node_id 1 to create a new ResNet object from inherited base classes: nn with example: obj = ResNet(block, layers, zero_init_residual, groups, widen, width_per_group, replace_stride_with_dilation, norm_layer, first_conv3x3, remove_first_maxpool, in_chans)