class_docstr: This class implements the Cnn14 model from https://arxiv.org/abs/1912.10211
  
  Arguments
  ---------
  mel_bins : int
      Number of mel frequency bins in the input
  emb_dim : int
      The dimensionality of the output embeddings
  norm_type: str in ['bn', 'in', 'ln']
      The type of normalization
  return_reps: bool (default=False)
      If True the model returns intermediate representations as well for interpretation
  
  Example:
  --------
  >>> cnn14 = Cnn14(120, 256)
  >>> x = torch.rand(3, 400, 120)
  >>> h = cnn14.forward(x)
  >>> print(h.shape)
  torch.Size([3, 1, 256])
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docstr: Initializes the model batch norm layer
docstr_tok: ["Initializes","the","model","batch","norm","layer"]
code: def init_weight(self):
      """
      Initializes the model batch norm layer
      """
      init_bn(self.norm0)
  
file_path: speechbrain/speechbrain/lobes/models/Cnn14.py
imports: import torch.nn
  import torch.nn.functional
  import torch
label_desc: Use image node_id 2 for calling the Cnn14 obj's underlying member method code with example usage: obj.init_weight() without return types