code: def __init__(
      self,
      checkpoint_path,
      layer=6,
      feat_hop_size=320,
      max_chunk=100 * 16_000,
      lazy_load=False,
  ):
      super().__init__()
      # NB: fairseq doesn't support pathlib.Path
      self.checkpoint_path = str(checkpoint_path)
      self.should_normalize = False
      self.lazy_load = lazy_load
      self.model = None
      self.layer = layer
      self.feat_hop_size = feat_hop_size
      self.max_chunk = max_chunk
      # this is useful for determining the device
      self.register_buffer(
          "_float_tensor", torch.tensor([0], dtype=torch.float)
      )
      if not self.lazy_load:
          self.load_checkpoint_()
  
file_path: textlesslib/textless/data/hubert_feature_reader.py
imports: import torch
  import fairseq
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new HubertFeatureReader object from inherited base classes: torch.nn with example: obj = HubertFeatureReader(checkpoint_path, layer, feat_hop_size, max_chunk, lazy_load)