code: def __init__(
      self,
      inplanes,
      planes,
      reps,
      stride=1,
      dilation=1,
      start_with_relu=True,
      grow_first=True,
      is_last=False,
  ):
      super(Block, self).__init__()
  
      if planes != inplanes or stride != 1:
          self.skip = nn.Conv2d(
              inplanes, planes, 1, stride=2, bias=False
          )
          if is_last:
              self.skip = nn.Conv2d(
                  inplanes, planes, 1, stride=1, bias=False
              )
          self.skipbn = nn.BatchNorm2d(planes)
      else:
          self.skip = None
  
      self.relu = nn.ReLU(inplace=True)
      rep = []
  
      filters = inplanes
      if grow_first:
          rep.append(self.relu)
          rep.append(
              SeparableConv2d_same(
                  inplanes, planes, 3, stride=1, dilation=dilation
              )
          )
          #             rep.append(nn.BatchNorm2d(planes))
          filters = planes
  
      for i in range(reps - 1):
          rep.append(self.relu)
          rep.append(
              SeparableConv2d_same(
                  filters, filters, 3, stride=1, dilation=dilation
              )
          )
      #             rep.append(nn.BatchNorm2d(filters))
  
      if not grow_first:
          rep.append(self.relu)
          rep.append(
              SeparableConv2d_same(
                  inplanes, planes, 3, stride=1, dilation=dilation
              )
          )
      #             rep.append(nn.BatchNorm2d(planes))
  
      if not start_with_relu:
          rep = rep[1:]
  
      if stride != 1:
          rep.append(self.relu)
          rep.append(
              SeparableConv2d_same(
                  planes, planes, 3, stride=2, dilation=dilation
              )
          )
  
      if is_last:
          rep.append(self.relu)
          rep.append(
              SeparableConv2d_same(
                  planes, planes, 3, stride=1, dilation=dilation
              )
          )
  
      self.rep = nn.Sequential(*rep)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from collections import OrderedDict
  import torch.utils.model_zoo
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: nn with example: obj = Block(inplanes, planes, reps, stride, dilation, start_with_relu, grow_first, is_last)