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","."]
docstr: Pass the input through Prenet.
  
  Args:
      x (Tensor): The input sequence to Prenet with shape (n_batch, in_dim).
  
  Return:
      x (Tensor): Tensor with shape (n_batch, sizes[-1])
docstr_tok: ["Pass","the","input","through","Prenet",".","Args",":","x","(","Tensor",")",":","The","input","sequence","to","Prenet","with","shape","(","n_batch",",","in_dim",")",".","Return",":","x","(","Tensor",")",":","Tensor","with","shape","(","n_batch",",","sizes","[","-1","]",")"]
code: def forward(self, x: Tensor) -> Tensor:
      r"""Pass the input through Prenet.
  
      Args:
          x (Tensor): The input sequence to Prenet with shape (n_batch, in_dim).
  
      Return:
          x (Tensor): Tensor with shape (n_batch, sizes[-1])
      """
  
      for linear in self.layers:
          x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
      return x
  
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 2 for calling the _Prenet obj's underlying member method code with example usage: obj.forward(x) and returns: x