class_docstr: Prenet Module. It is consists of ``len(output_size)`` linear layers.
  
  Args:
      in_dim (int): The size of each input sample.
      output_sizes (list): The output dimension of each linear layers.
class_docstr_tok: ["Prenet","Module",".","It","is","consists","of","``","len","(","output_size",")","``","linear","layers",".","Args",":","in_dim","(","int",")",":","The","size","of","each","input","sample",".","output_sizes","(","list",")",":","The","output","dimension","of","each","linear","layers","."]
code: def __init__(self, in_dim: int, out_sizes: List[int]) -> None:
      super().__init__()
      in_sizes = [in_dim] + out_sizes[:-1]
      self.layers = nn.ModuleList(
          [
              _get_linear_layer(in_size, out_size, bias=False)
              for (in_size, out_size) in zip(in_sizes, out_sizes)
          ]
      )
  
imports: import warnings
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new _Prenet object from inherited base classes: nn with example: obj = _Prenet(in_dim, out_sizes)