code: def ctc_greedy_decode(probabilities, seq_lens, blank_id=-1):
      """Greedy decode a batch of probabilities and apply CTC rules.
  
      Arguments
      ---------
      probabilities : torch.tensor
          Output probabilities (or log-probabilities) from the network with shape
          [batch, probabilities, time]
      seq_lens : torch.tensor
          Relative true sequence lengths (to deal with padded inputs),
          the longest sequence has length 1.0, others a value between zero and one
          shape [batch, lengths].
      blank_id : int, string
          The blank symbol/index. Default: -1. If a negative number is given,
          it is assumed to mean counting down from the maximum possible index,
          so that -1 refers to the maximum possible index.
  
      Returns
      -------
      list
          Outputs as Python list of lists, with "ragged" dimensions; padding
          has been removed.
  
      Example
      -------
      >>> import torch
      >>> probs = torch.tensor([[[0.3, 0.7], [0.0, 0.0]],
      ...                       [[0.2, 0.8], [0.9, 0.1]]])
      >>> lens = torch.tensor([0.51, 1.0])
      >>> blank_id = 0
      >>> ctc_greedy_decode(probs, lens, blank_id)
      [[1], [1]]
      """
      if isinstance(blank_id, int) and blank_id < 0:
          blank_id = probabilities.shape[-1] + blank_id
      batch_max_len = probabilities.shape[1]
      batch_outputs = []
      for seq, seq_len in zip(probabilities, seq_lens):
          actual_size = int(torch.round(seq_len * batch_max_len))
          scores, predictions = torch.max(
              seq.narrow(0, 0, actual_size), dim=1
          )
          out = filter_ctc_output(
              predictions.tolist(), blank_id=blank_id
          )
          batch_outputs.append(out)
      return batch_outputs
  
imports: from itertools import groupby
  from speechbrain.dataio.dataio import length_to_mask
  import math
  import dataclasses
  import numpy
  import heapq
  import logging
  import torch
  import warnings
  from typing import Dict, List, Optional, Union, Any, Tuple
label_desc: Use image node_id 2 for calling a global function with example usage: ctc_greedy_decode(probabilities, seq_lens, blank_id) and returns: batch_outputs