code: def _handle_corner_cases_during_shifting(
      alignments: Dict[int, int],
      pred_errors: List[int],
      target_errors: List[int],
      pred_start: int,
      target_start: int,
      length: int,
  ) -> bool:
      """Return ``True`` if any of corner cases has been met. Otherwise, ``False`` is returned.
  
      Args:
          alignments: A dictionary mapping aligned positions between a reference and a hypothesis.
          pred_errors: A list of error positions in a hypothesis.
          target_errors: A list of error positions in a reference.
          pred_start: A hypothesis start index.
          target_start: A reference start index.
          length: A length of a word span to be considered.
  
      Return:
          An indication whether any of conrner cases has been met.
  
      """
      # don't do the shift unless both the hypothesis was wrong and the
      # reference doesn't match hypothesis at the target position
      if sum(pred_errors[pred_start : pred_start + length]) == 0:
          return True
  
      if sum(target_errors[target_start : target_start + length]) == 0:
          return True
  
      # don't try to shift within the subsequence
      if pred_start <= alignments[target_start] < pred_start + length:
          return True
  
      return False
  
imports: import re
  from functools import lru_cache
  from typing import Dict, Iterator, List, Optional, Sequence, Tuple, Type, Union
  from torch import Tensor, tensor
  from torchmetrics.functional.text.helper import _flip_trace, _LevenshteinEditDistance, _trace_to_alignment, _validate_inputs
label_desc: Use image node_id 3 for calling a global function with example usage: _handle_corner_cases_during_shifting(alignments, pred_errors, target_errors, pred_start, target_start, length) and returns: False, True, True, True