code: def __init__(
      self,
      inplanes,
      planes,
      stride=1,
      downsample=None,
      groups=1,
      base_width=64,
      dilation=1,
      norm_layer=None,
  ):
      super().__init__()
      if norm_layer is None:
          norm_layer = nn.BatchNorm2d
      if groups != 1 or base_width != 64:
          raise ValueError(
              "BasicBlock only supports groups=1 and base_width=64"
          )
      if dilation > 1:
          raise NotImplementedError(
              "Dilation > 1 not supported in BasicBlock"
          )
      # Both self.conv1 and self.downsample layers downsample the input when stride != 1
      self.conv1 = conv3x3(inplanes, planes, stride)
      self.bn1 = norm_layer(planes)
      self.relu = nn.ReLU(inplace=True)
      self.conv2 = conv3x3(planes, planes)
      self.bn2 = norm_layer(planes)
      self.downsample = downsample
      self.stride = stride
  
imports: from collections import OrderedDict
  import torch
  from torch import nn
  from torch.jit.annotations import Dict
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new BasicBlock object from inherited base classes: nn with example: obj = BasicBlock(inplanes, planes, stride, downsample, groups, base_width, dilation, norm_layer)