code: def __init__(
      self,
      in_channels,
      out_channels,
      res2net_scale=8,
      se_channels=128,
      kernel_size=1,
      dilation=1,
      activation=nn.ReLU,
  ):
      super(SERes2NetBlock, self).__init__()
      self.out_channels = out_channels
      self.tdnn1 = TDNNBlock(
          in_channels,
          out_channels,
          kernel_size=1,
          dilation=1,
          activation=activation,
      )
      self.res2net_block = Res2NetBlock(
          out_channels, out_channels, res2net_scale, dilation
      )
      self.tdnn2 = TDNNBlock(
          out_channels,
          out_channels,
          kernel_size=1,
          dilation=1,
          activation=activation,
      )
      self.se_block = SEBlock(out_channels, se_channels, out_channels)
  
      self.shortcut = None
      if in_channels != out_channels:
          self.shortcut = Conv1d(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=1,
          )
  
imports: import math
  import os
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new SERes2NetBlock object from inherited base classes: nn with example: obj = SERes2NetBlock(in_channels, out_channels, res2net_scale, se_channels, kernel_size, dilation, activation)