class_docstr: Dataset to read the score file containing the beams and their score
  
  Args:
      data_path: path to the beams file
      tokenizer: tokenizer of the LM model
      manifest_path: manifest `.json` file which contains the ground truths transcripts
      beam_size: the number of beams per sample
      max_seq_length: the maximum length of sequences
class_docstr_tok: ["Dataset","to","read","the","score","file","containing","the","beams","and","their","score","Args",":","data_path",":","path","to","the","beams","file","tokenizer",":","tokenizer","of","the","LM","model","manifest_path",":","manifest","`",".json","`","file","which","contains","the","ground","truths","transcripts","beam_size",":","the","number","of","beams","per","sample","max_seq_length",":","the","maximum","length","of","sequences"]
code: def __len__(self):
      return len(self.data)
  
imports: import contextlib
  import inspect
  import json
  from argparse import ArgumentParser
  import editdistance
  import matplotlib.pyplot
  import numpy
  import pandas
  import torch
  import tqdm
  from transformers import AutoModelForCausalLM
  from nemo.collections.nlp.models.language_modeling import TransformerLMModel
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the BeamScoresDataset obj's underlying member method code with example usage: obj.__len__() and returns: len