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: Does forward dynamic programming algorithm.
  
  Arguments
  ---------
  pi_prob : torch.Tensor (batch, phn)
      Tensor containing initial (log) probabilities.
  trans_prob : torch.Tensor (batch, from, to)
      Tensor containing transition (log) probabilities.
  emiss_pred_useful : torch.Tensor (batch, phoneme in phn sequence, time)
      A 'useful' form of the posterior probabilities, rearranged
      into the order of phoneme appearance in phns.
  lens_abs : torch.Tensor (batch)
      The absolute length of each input to the acoustic model,
      i.e., the number of frames.
  phn_lens_abs : torch.Tensor (batch)
      The absolute length of each phoneme sequence in the batch.
  phns : torch.Tensor (batch, phoneme in phn sequence)
      The phonemes that are known/thought to be in each utterance.
  
  Returns
  -------
  sum_alpha_T : torch.Tensor (batch)
      The (log) likelihood of each utterance in the batch.
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code: def _dp_forward(
      self,
      pi_prob,
      trans_prob,
      emiss_pred_useful,
      lens_abs,
      phn_lens_abs,
      phns,
  ):
      """Does forward dynamic programming algorithm.
  
      Arguments
      ---------
      pi_prob : torch.Tensor (batch, phn)
          Tensor containing initial (log) probabilities.
      trans_prob : torch.Tensor (batch, from, to)
          Tensor containing transition (log) probabilities.
      emiss_pred_useful : torch.Tensor (batch, phoneme in phn sequence, time)
          A 'useful' form of the posterior probabilities, rearranged
          into the order of phoneme appearance in phns.
      lens_abs : torch.Tensor (batch)
          The absolute length of each input to the acoustic model,
          i.e., the number of frames.
      phn_lens_abs : torch.Tensor (batch)
          The absolute length of each phoneme sequence in the batch.
      phns : torch.Tensor (batch, phoneme in phn sequence)
          The phonemes that are known/thought to be in each utterance.
  
      Returns
      -------
      sum_alpha_T : torch.Tensor (batch)
          The (log) likelihood of each utterance in the batch.
      """
      # useful values
      batch_size = len(phn_lens_abs)
      U_max = phn_lens_abs.max()
      fb_max_length = lens_abs.max()
      device = emiss_pred_useful.device
  
      pi_prob = pi_prob.to(device)
      trans_prob = trans_prob.to(device)
  
      # initialise
      alpha_matrix = self.neg_inf * torch.ones(
          [batch_size, U_max, fb_max_length], device=device
      )
      alpha_matrix[:, :, 0] = pi_prob + emiss_pred_useful[:, :, 0]
  
      for t in range(1, fb_max_length):
          utt_lens_passed = lens_abs < t
  
          if True in utt_lens_passed:
              n_passed = utt_lens_passed.sum()
              I_tensor = self.neg_inf * torch.ones(
                  n_passed, U_max, U_max
              )
              I_tensor[
                  :, torch.arange(U_max), torch.arange(U_max)
              ] = 0.0
              I_tensor = I_tensor.to(device)
  
              trans_prob[utt_lens_passed] = I_tensor
  
          alpha_times_trans = batch_log_matvecmul(
              trans_prob.permute(0, 2, 1), alpha_matrix[:, :, t - 1]
          )
          alpha_matrix[:, :, t] = (
              alpha_times_trans + emiss_pred_useful[:, :, t]
          )
  
      sum_alpha_T = torch.logsumexp(
          alpha_matrix[torch.arange(batch_size), :, -1], dim=1
      )
  
      return sum_alpha_T
  
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 7 for calling the HMMAligner obj's underlying member method code with example usage: obj._dp_forward(pi_prob, trans_prob, emiss_pred_useful, lens_abs, phn_lens_abs, phns) and returns: sum_alpha_T