class_docstr: Neural Module that does CTC beam search with a N-gram language model.
  It takes a batch of log_probabilities. Note the bigger the batch, the
  better as processing is parallelized. Outputs a list of size batch_size.
  Each element in the list is a list of size beam_search, and each element
  in that list is a tuple of (final_log_prob, hyp_string).
  Args:
      vocab (list): List of characters that can be output by the ASR model. For English, this is the 28 character set
          {a-z '}. The CTC blank symbol is automatically added.
      beam_width (int): Size of beams to keep and expand upon. Larger beams result in more accurate but slower
          predictions
      alpha (float): The amount of importance to place on the N-gram language model. Larger alpha means more
          importance on the LM and less importance on the acoustic model.
      beta (float): A penalty term given to longer word sequences. Larger beta will result in shorter sequences.
      lm_path (str): Path to N-gram language model
      num_cpus (int): Number of CPUs to use
      cutoff_prob (float): Cutoff probability in vocabulary pruning, default 1.0, no pruning
      cutoff_top_n (int): Cutoff number in pruning, only top cutoff_top_n characters with highest probs in
          vocabulary will be used in beam search, default 40.
      input_tensor (bool): Set to True if you intend to pass PyTorch Tensors, set to False if you intend to pass
          NumPy arrays.
code: def forward(self, log_probs, log_probs_length):
      probs_list = log_probs
      if self.input_tensor:
          probs = torch.exp(log_probs)
          probs_list = []
          for i, prob in enumerate(probs):
              probs_list.append(prob[: log_probs_length[i], :])
      res = self.beam_search_func(
          probs_list,
          self.vocab,
          beam_size=self.beam_width,
          num_processes=self.num_cpus,
          ext_scoring_func=self.scorer,
          cutoff_prob=self.cutoff_prob,
          cutoff_top_n=self.cutoff_top_n,
      )
      return res
  
file_path: NeMo/nemo/collections/asr/modules/beam_search_decoder.py
imports: import torch
  from nemo.core.classes import NeuralModule, typecheck
  from nemo.core.neural_types import LengthsType, LogprobsType, NeuralType, PredictionsType
label_desc: Use image node_id 4 for calling the BeamSearchDecoderWithLM obj's underlying member method code with example usage: obj.forward(log_probs, log_probs_length) and returns: res