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: Creates tensor of initial (log) probabilities (known as 'pi').
  Assigns all probability mass to the first phoneme in the sequence.
  
  Arguments
  ---------
  phn_lens_abs : torch.Tensor (batch)
      The absolute length of each phoneme sequence in the batch.
  
  Returns
  -------
  pi_prob : torch.Tensor (batch, phn)
docstr_tok: ["Creates","tensor","of","initial","(","log",")","probabilities","(","known","as","'pi","'",")",".","Assigns","all","probability","mass","to","the","first","phoneme","in","the","sequence",".","Arguments","--","--","--","--","-","phn_lens_abs",":","torch.Tensor","(","batch",")","The","absolute","length","of","each","phoneme","sequence","in","the","batch",".","Returns","--","--","--","-","pi_prob",":","torch.Tensor","(","batch",",","phn",")"]
code: def _make_pi_prob(self, phn_lens_abs):
      """Creates tensor of initial (log) probabilities (known as 'pi').
      Assigns all probability mass to the first phoneme in the sequence.
  
      Arguments
      ---------
      phn_lens_abs : torch.Tensor (batch)
          The absolute length of each phoneme sequence in the batch.
  
      Returns
      -------
      pi_prob : torch.Tensor (batch, phn)
      """
      batch_size = len(phn_lens_abs)
      U_max = int(phn_lens_abs.max())
  
      pi_prob = self.neg_inf * torch.ones([batch_size, U_max])
      pi_prob[:, 0] = 0
  
      return pi_prob
  
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 4 for calling the HMMAligner obj's underlying member method code with example usage: obj._make_pi_prob(phn_lens_abs) and returns: pi_prob