code: def __init__(
      self,
      in_filters,
      out_filters,
      reps,
      strides=1,
      start_with_relu=True,
      grow_first=True,
  ):
      super(Block, self).__init__()
  
      if out_filters != in_filters or strides != 1:
          self.skip = nn.Conv2d(
              in_filters, out_filters, 1, stride=strides, bias=False
          )
          self.skipbn = nn.BatchNorm2d(out_filters)
      else:
          self.skip = None
  
      rep = []
  
      filters = in_filters
      if grow_first:
          rep.append(nn.ReLU(inplace=True))
          rep.append(
              SeparableConv2d(
                  in_filters,
                  out_filters,
                  3,
                  stride=1,
                  padding=1,
                  bias=False,
              )
          )
          rep.append(nn.BatchNorm2d(out_filters))
          filters = out_filters
  
      for i in range(reps - 1):
          rep.append(nn.ReLU(inplace=True))
          rep.append(
              SeparableConv2d(
                  filters, filters, 3, stride=1, padding=1, bias=False
              )
          )
          rep.append(nn.BatchNorm2d(filters))
  
      if not grow_first:
          rep.append(nn.ReLU(inplace=True))
          rep.append(
              SeparableConv2d(
                  in_filters,
                  out_filters,
                  3,
                  stride=1,
                  padding=1,
                  bias=False,
              )
          )
          rep.append(nn.BatchNorm2d(out_filters))
  
      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)
  
file_path: pywick/pywick/models/classification/xception1.py
imports: import torch.nn
  import torch.nn.functional
  import torch.utils.model_zoo
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: nn with example: obj = Block(in_filters, out_filters, reps, strides, start_with_relu, grow_first)