code: def __init__(
      self,
      in_channels,
      out_channels,
      reps,
      strides=1,
      start_with_relu=True,
      grow_first=True,
  ):
      super(Block, self).__init__()
  
      if out_channels != in_channels or strides != 1:
          self.skip = nn.Conv2d(
              in_channels, out_channels, 1, stride=strides, bias=False
          )
          self.skipbn = nn.BatchNorm2d(out_channels)
      else:
          self.skip = None
  
      rep = []
      for i in range(reps):
          if grow_first:
              inc = in_channels if i == 0 else out_channels
              outc = out_channels
          else:
              inc = in_channels
              outc = in_channels if i < (reps - 1) else out_channels
          rep.append(nn.ReLU(inplace=True))
          rep.append(SeparableConv2d(inc, outc, 3, stride=1, padding=1))
          rep.append(nn.BatchNorm2d(outc))
  
      if not start_with_relu:
          rep = rep[1:]
      else:
          rep[0] = nn.ReLU(inplace=False)
  
      if strides != 1:
          rep.append(nn.MaxPool2d(3, strides, 1))
      self.rep = nn.Sequential(*rep)
  
imports: import torch.jit
  import torch.nn
  import torch.nn.functional
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: nn with example: obj = Block(in_channels, out_channels, reps, strides, start_with_relu, grow_first)