class_docstr: FairSeq Hubert encoder module, used for loading pretrained weight and finetuning
  
  Args:
      input_size: input dim
      hubert_url: url to Hubert pretrained model
      hubert_dir_path: directory to download the Wav2Vec2.0 pretrained model.
      output_size: dimension of attention
      normalize_before: whether to use layer_norm before the first block
      freeze_finetune_updates: steps that freeze all layers except output layer
          before tuning the whole model (nessasary to prevent overfit).
      dropout_rate: dropout rate
      activation_dropout: dropout rate in activation function
      attention_dropout: dropout rate in attention
  Hubert specific Args:
      Please refer to:
      https://github.com/pytorch/fairseq/blob/master/fairseq/models/hubert/hubert.py
code: def reload_pretrained_parameters(self):
      self.encoders.load_state_dict(
          self.pretrained_params, strict=False
      )
      logging.info("Pretrained Hubert model parameters reloaded!")
  
imports: import contextlib
  import copy
  import logging
  import os
  from pathlib import Path
  from typing import List, Optional, Tuple
  import torch
  import yaml
  from filelock import FileLock
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
label_desc: Use image node_id 4 for calling the FairseqHubertEncoder obj's underlying member method code with example usage: obj.reload_pretrained_parameters() without return types