class_docstr: This class calculates Viterbi alignments in the forward method.
  
  It also records alignments and creates batches of them for use
  in Viterbi training.
  
  Arguments
  ---------
  states_per_phoneme : int
      Number of hidden states to use per phoneme.
  output_folder : str
      It is the folder that the alignments will be stored in when
      saved to disk. Not yet implemented.
  neg_inf : float
      The float used to represent a negative infinite log probability.
      Using `-float("Inf")` tends to give numerical instability.
      A number more negative than -1e5 also sometimes gave errors when
      the `genbmm` library was used (currently not in use). (default: -1e5)
  batch_reduction : string
      One of "none", "sum" or "mean".
      What kind of batch-level reduction to apply to the loss calculated
      in the forward method.
  input_len_norm : bool
      Whether to normalize the loss in the forward method by the length of
      the inputs.
  target_len_norm : bool
      Whether to normalize the loss in the forward method by the length of
      the targets.
  lexicon_path : string
      The location of the lexicon.
  
  Example
  -------
  >>> log_posteriors = torch.tensor([[[ -1., -10., -10.],
  ...                                 [-10.,  -1., -10.],
  ...                                 [-10., -10.,  -1.]],
  ...
  ...                                [[ -1., -10., -10.],
  ...                                 [-10.,  -1., -10.],
  ...                                 [-10., -10., -10.]]])
  >>> lens = torch.tensor([1., 0.66])
  >>> phns = torch.tensor([[0, 1, 2],
  ...                      [0, 1, 0]])
  >>> phn_lens = torch.tensor([1., 0.66])
  >>> aligner = HMMAligner()
  >>> forward_scores = aligner(
  ...        log_posteriors, lens, phns, phn_lens, 'forward'
  ... )
  >>> forward_scores.shape
  torch.Size([2])
  >>> viterbi_scores, alignments = aligner(
  ...        log_posteriors, lens, phns, phn_lens, 'viterbi'
  ... )
  >>> alignments
  [[0, 1, 2], [0, 1]]
  >>> viterbi_scores.shape
  torch.Size([2])
docstr: Retrieves Viterbi alignments stored in `self.align_dict` and
  creates a batch of them, with zero padding.
  
  Arguments
  ---------
  ids : list of str
      IDs of the files in the batch.
  lens_abs : torch.Tensor (batch)
      The absolute length of each input to the acoustic model,
      i.e., the number of frames.
  
  Returns
  -------
  viterbi_batch : torch.Tensor (batch, time)
      The previously-recorded Viterbi alignments for the utterances
      in the batch.
docstr_tok: ["Retrieves","Viterbi","alignments","stored","in","`","self.align_dict","`","and","creates","a","batch","of","them",",","with","zero","padding",".","Arguments","--","--","--","--","-","ids",":","list","of","str","IDs","of","the","files","in","the","batch",".","lens_abs",":","torch.Tensor","(","batch",")","The","absolute","length","of","each","input","to","the","acoustic","model",",","i.e.",",","the","number","of","frames",".","Returns","--","--","--","-","viterbi_batch",":","torch.Tensor","(","batch",",","time",")","The","previously-recorded","Viterbi","alignments","for","the","utterances","in","the","batch","."]
code: def _get_viterbi_batch(self, ids, lens_abs):
      """Retrieves Viterbi alignments stored in `self.align_dict` and
      creates a batch of them, with zero padding.
  
      Arguments
      ---------
      ids : list of str
          IDs of the files in the batch.
      lens_abs : torch.Tensor (batch)
          The absolute length of each input to the acoustic model,
          i.e., the number of frames.
  
      Returns
      -------
      viterbi_batch : torch.Tensor (batch, time)
          The previously-recorded Viterbi alignments for the utterances
          in the batch.
  
      """
      batch_size = len(lens_abs)
      fb_max_length = torch.max(lens_abs)
  
      viterbi_batch = torch.zeros(
          batch_size, fb_max_length, device=lens_abs.device
      ).long()
      for i in range(batch_size):
          viterbi_preds = self.align_dict[ids[i]]
          viterbi_preds = torch.nn.functional.pad(
              viterbi_preds, (0, fb_max_length - len(viterbi_preds))
          )
  
          viterbi_batch[i] = viterbi_preds.long()
  
      return viterbi_batch
  
imports: import torch
  import random
  from speechbrain.utils.checkpoints import register_checkpoint_hooks
  from speechbrain.utils.checkpoints import mark_as_saver
  from speechbrain.utils.checkpoints import mark_as_loader
  from speechbrain.utils.data_utils import undo_padding
label_desc: Use image node_id 14 for calling the HMMAligner obj's underlying member method code with example usage: obj._get_viterbi_batch(ids, lens_abs) and returns: viterbi_batch