class_docstr: The Tacotron pre-net module consisting of a specified number of
  normalized (Xavier-initialized) linear layers
  
  Arguments
  ---------
  in_dim: int
      the input dimensions
  sizes: int
      the dimension of the hidden layers/outout
  dropout: float
      the dropout probability
  
  Example
  -------
  >>> import torch
  >>> from speechbrain.lobes.models.Tacotron2 import Prenet
  >>> layer = Prenet()
  >>> x = torch.randn(862, 2, 80)
  >>> output = layer(x)
  >>> output.shape
  torch.Size([862, 2, 256])
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docstr: Computes the forward pass for the prenet
  
  Arguments
  ---------
  x: torch.Tensor
      the prenet inputs
  
  Returns
  -------
  output: torch.Tensor
      the output
docstr_tok: ["Computes","the","forward","pass","for","the","prenet","Arguments","--","--","--","--","-","x",":","torch.Tensor","the","prenet","inputs","Returns","--","--","--","-","output",":","torch.Tensor","the","output"]
code: def forward(self, x):
      """Computes the forward pass for the prenet
  
      Arguments
      ---------
      x: torch.Tensor
          the prenet inputs
  
      Returns
      -------
      output: torch.Tensor
          the output
      """
      for linear in self.layers:
          x = F.dropout(
              F.relu(linear(x)), p=self.dropout, training=True
          )
      return x
  
imports: from math import sqrt
  from speechbrain.nnet.loss.guidedattn_loss import GuidedAttentionLoss
  from speechbrain.lobes.models.transformer.Transformer import get_mask_from_lengths
  import torch
  from torch import nn
  from torch.nn import functional
  from collections import namedtuple
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