code: def __init__(self, channels: int, bottleneck: int = 128):
      super().__init__()
      self.se = nn.Sequential(
          nn.AdaptiveAvgPool1d(1),
          nn.Conv1d(channels, bottleneck, kernel_size=1, padding=0),
          nn.ReLU(),
          nn.BatchNorm1d(bottleneck),
          nn.Conv1d(bottleneck, channels, kernel_size=1, padding=0),
          nn.Sigmoid(),
      )
  
file_path: espnet/espnet2/spk/layers/ecapa_block.py
imports: import math
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new SEModule object from inherited base classes: nn with example: obj = SEModule(channels, bottleneck)