class_docstr: FairSeq Hubert pretrain encoder module, only used for pretraining stage
  
  Args:
      input_size: input dim
      output_size: dimension of attention
      linear_units: dimension of feedforward layers
      attention_heads: the number of heads of multi head attention
      num_blocks: the number of encoder blocks
      dropout_rate: dropout rate
      attention_dropout_rate: dropout rate in attention
      hubert_dict: target dictionary for Hubert pretraining
      label_rate: label frame rate. -1 for sequence label
      sample_rate: target sample rate.
      use_amp: whether to use automatic mixed precision
      normalize_before: whether to use layer_norm before the first block
code: def _build_dictionary(self, dictionary, hubert_dict_path):
      if os.path.exists(f"{hubert_dict_path}"):
          setattr(dictionary, "symbols", [])
          setattr(dictionary, "count", [])
          setattr(dictionary, "indices", {})
          dictionary.add_from_file(f"{hubert_dict_path}")
      else:
          dictionary.add_symbol("0")
  
      self.dictionaries = [dictionary]
  
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 2 for calling the FairseqHubertPretrainEncoder obj's underlying member method code with example usage: obj._build_dictionary(dictionary, hubert_dict_path) without return types