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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code: def __init__(self, in_dim=80, sizes=[256, 256], dropout=0.5):
      super().__init__()
      in_sizes = [in_dim] + sizes[:-1]
      self.layers = nn.ModuleList(
          [
              LinearNorm(in_size, out_size, bias=False)
              for (in_size, out_size) in zip(in_sizes, sizes)
          ]
      )
      self.dropout = dropout
  
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 1 to create a new Prenet object from inherited base classes: nn with example: obj = Prenet(in_dim, sizes, dropout)