class_docstr: RNN-T loss implementation based on WFST according
  to "Powerful and Extensible WFST Framework for RNN-Transducer Losses"
  https://ieeexplore.ieee.org/document/10096679
docstr: Relabel states to be in topological order: by diagonals
  
  Args:
      states: tensor with states
      n: number of rows
      m: number of columns
  
  Returns:
      tensor with relabeled states (same shape as `states`)
docstr_tok: ["Relabel","states","to","be","in","topological","order",":","by","diagonals","Args",":","states",":","tensor","with","states","n",":","number","of","rows","m",":","number","of","columns","Returns",":","tensor","with","relabeled","states","(","same","shape","as","`","states","`",")"]
code: def relabel_states(
      states: torch.Tensor, n: int, m: int
  ) -> torch.Tensor:
      """
      Relabel states to be in topological order: by diagonals
  
      Args:
          states: tensor with states
          n: number of rows
          m: number of columns
  
      Returns:
          tensor with relabeled states (same shape as `states`)
      """
      i = states % n
      j = torch.div(
          states, n, rounding_mode="floor"
      )  # states // n, torch.div to avoid pytorch warnings
      min_mn = min(m, n)
      max_mn = max(m, n)
      diag = i + j
      anti_diag = m + n - 1 - diag
      max_idx = n * m - 1
      cur_diag_idx = i if m > n else m - j - 1
      states = (
          diag.lt(min_mn) * ((diag * (diag + 1) >> 1) + i)
          + torch.logical_and(diag.ge(min_mn), diag.lt(max_mn))
          * (
              (min_mn * (min_mn + 1) >> 1)
              + (diag - min_mn) * min_mn
              + cur_diag_idx
          )
          + diag.ge(max_mn)
          * (max_idx - (anti_diag * (anti_diag + 1) >> 1) + m - j)
      )
      return states
  
imports: import abc
  from contextlib import nullcontext
  from typing import ContextManager
  import torch
  import torch.nn.functional
  from nemo.core.classes.loss import Loss
  from nemo.core.utils.k2_guard import k2
label_desc: Use image node_id 4 for calling the GraphRnntLoss obj's underlying member method code with example usage: obj.relabel_states(states, n, m) and returns: states