class_docstr: This lobes enables the integration of fairseq pretrained wav2vec1.0 models.
  
  Arguments
  ---------
  pretrained_path : str
      Path of the pretrained wav2vec1 model. It can be a url or a local path.
  save_path : str
      Path and filename of the downloaded model.
  output_norm : bool (default: True)
      If True, a layer_norm (affine) will be applied to the output obtained
      from the wav2vec model.
  freeze : bool (default: True)
      If True, the model is frozen. If False, the model will be trained
      alongside with the rest of the pipeline.
  pretrain : bool (default: True)
      If True, the model is pretrained with the specified source.
      If False, the randomly-initialized model is instantiated.
  
  Example
  -------
  >>> inputs = torch.rand([10, 600])
  >>> model_url = ""
  >>> save_path = "models_checkpoints/wav2vec.pt"
  >>> model = FairseqWav2Vec1(model_url, save_path)
  >>> outputs = model(inputs)
  >>> outputs.shape
  torch.Size([10, 100, 512])
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docstr: Reinitializes the parameters of the network
docstr_tok: ["Reinitializes","the","parameters","of","the","network"]
code: def reset_layer(self, model):
      """Reinitializes the parameters of the network"""
      if hasattr(model, "reset_parameters"):
          model.reset_parameters()
      for child_layer in model.children():
          if model != child_layer:
              self.reset_layer(child_layer)
  
imports: import torch
  import logging
  import torch.nn.functional
  from torch import nn
  from speechbrain.utils.data_utils import download_file
  from speechbrain.dataio.dataio import length_to_mask
label_desc: Use image node_id 4 for calling the FairseqWav2Vec1 obj's underlying member method code with example usage: obj.reset_layer(model) without return types