class_docstr: Encoder module that transform 1D waveform to 2D representations.
  
  Args:
      feat_dim (int, optional): The feature dimension after Encoder module. (Default: 512)
      win_len (int, optional): kernel size in the Conv1D layer. (Default: 32)
code: def __init__(self, feat_dim: int = 512, win_len: int = 32) -> None:
      super(Encoder, self).__init__()
  
      self.conv1d = nn.Conv1d(
          1, feat_dim, win_len, stride=win_len // 2, bias=False
      )
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Encoder object from inherited base classes: nn with example: obj = Encoder(feat_dim, win_len)