code: def __init__(
      self,
      input_size,
      lin_neurons=192,
      activation=nn.ReLU,
      channels=[512, 512, 512, 512, 1536],
      kernel_sizes=[5, 3, 3, 3, 1],
      dilations=[1, 2, 3, 4, 1],
      attention_channels=128,
      res2net_scale=8,
      se_channels=128,
      global_context=True,
  ):
      super(ECAPA_TDNN, self).__init__()
      assert len(channels) == len(kernel_sizes)
      assert len(channels) == len(dilations)
      self.channels = channels
      self.blocks = nn.LayerList()
      self.emb_size = lin_neurons
  
      # The initial TDNN layer
      self.blocks.append(
          TDNNBlock(
              input_size,
              channels[0],
              kernel_sizes[0],
              dilations[0],
              activation,
          )
      )
  
      # SE-Res2Net layers
      for i in range(1, len(channels) - 1):
          self.blocks.append(
              SERes2NetBlock(
                  channels[i - 1],
                  channels[i],
                  res2net_scale=res2net_scale,
                  se_channels=se_channels,
                  kernel_size=kernel_sizes[i],
                  dilation=dilations[i],
                  activation=activation,
              )
          )
  
      # Multi-layer feature aggregation
      self.mfa = TDNNBlock(
          channels[-1],
          channels[-1],
          kernel_sizes[-1],
          dilations[-1],
          activation,
      )
  
      # Attentive Statistical Pooling
      self.asp = AttentiveStatisticsPooling(
          channels[-1],
          attention_channels=attention_channels,
          global_context=global_context,
      )
      self.asp_bn = BatchNorm1d(input_size=channels[-1] * 2)
  
      # Final linear transformation
      self.fc = Conv1d(
          in_channels=channels[-1] * 2,
          out_channels=self.emb_size,
          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 ECAPA_TDNN object from inherited base classes: nn with example: obj = ECAPA_TDNN(input_size, lin_neurons, activation, channels, kernel_sizes, dilations, attention_channels, res2net_scale, se_channels, global_context)