code: import torch.nn as nn
  import math
  import torch.utils.model_zoo as model_zoo
  import torch.nn.init as weight_init
  import torch
  __all__ = ['MultipleBasicBlock','MultipleBasicBlock_4']
  def conv3x3(in_planes, out_planes, dilation = 1, stride=1):
      "3x3 convolution with padding"
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                       padding=int(dilation*(3-1)/2), dilation=dilation, bias=False)
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, dilation = 1, stride=1, downsample=None):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(inplanes, planes,dilation, stride)
          # self.bn1 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3(planes, planes)
          # self.bn2 = nn.BatchNorm2d(planes)
          self.downsample = downsample
          self.stride = stride
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
                  # weight_init.xavier_normal()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          # out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          # out = self.bn2(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  class MultipleBasicBlock(nn.Module):
  
      def __init__(self,input_feature,
                   block, num_blocks,
                   intermediate_feature = 64, dense = True):
          super(MultipleBasicBlock, self).__init__()
          self.dense = dense
          self.num_block = num_blocks
          self.intermediate_feature = intermediate_feature
  
          self.block1= nn.Sequential(*[
              nn.Conv2d(input_feature, intermediate_feature,
                        kernel_size=7, stride=1, padding=3, bias=False),
              nn.ReLU(inplace=True)
          ])
  
          # for i in range(1, num_blocks):
          self.block2 = block(intermediate_feature, intermediate_feature, dilation = 1) if num_blocks>=2 else None
          self.block3 = block(intermediate_feature, intermediate_feature, dilation = 1) if num_blocks>=3 else None
          self.block4 = block(intermediate_feature, intermediate_feature, dilation = 1) if num_blocks>=4 else None
          self.block5 = nn.Sequential(*[nn.Conv2d(intermediate_feature, 3 , (3, 3), 1, (1, 1))])
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def forward(self, x):
          x = self.block1(x)
          x = self.block2(x)
          x = self.block3(x)
          x = self.block4(x)
          x = self.block5(x)
          return x
  
  def MultipleBasicBlock_4(input_feature,intermediate_feature = 64):
      model = MultipleBasicBlock(input_feature,
                                 BasicBlock,4 ,
                                 intermediate_feature)
      return model
  
  
  if __name__ == '__main__':
  
      # x= Variable(torch.randn(2,3,224,448))
      # model =    S2DF(BasicBlock,3,True)
      # y = model(x)
      model = MultipleBasicBlock(200, BasicBlock,4)
      model = BasicBlock(64,64,1)
      # y = model(x)
      exit(0)
apis: torch.nn.ReLU torch.nn.Conv2d