code: def filter_by_token(
      token: str, issues: List[Tuple[int, int]], tokens: List[List[str]]
  ) -> List[Tuple[int, int]]:
      """
      Return subset of label issues involving a particular token.
  
      Parameters
      ----------
      token:
          A specific token you are interested in.
  
      issues:
          List of tuples ``(i, j)`` representing a label issue for the `j`-th token of the `i`-th sentence.
          Same format as output by :py:func:`token_classification.filter.find_label_issues <cleanlab.token_classification.filter.find_label_issues>`
          or :py:func:`token_classification.rank.issues_from_scores <cleanlab.token_classification.rank.issues_from_scores>`.
  
      tokens:
          Nested list such that `tokens[i]` is a list of tokens (strings/words) that comprise the `i`-th sentence.
  
      Returns
      ----------
      issues_subset:
          List of tuples ``(i, j)`` representing a label issue for the `j`-th token of the `i`-th sentence, in the same format as `issues`.
          But restricting to only those issues that involve the specified `token`.
  
      Examples
      --------
      >>> from cleanlab.token_classification.summary import filter_by_token
      >>> token = "?weird"
      >>> issues = [(2, 0), (0, 1)]
      >>> tokens = [
      ...     ["A", "?weird", "sentence"],
      ...     ["A", "valid", "sentence"],
      ...     ["An", "sentence", "with", "a", "typo"],
      ... ]
      >>> filter_by_token(token, issues, tokens)
      [(0, 1)]
      """
      returned_issues = []
      for issue in issues:
          i, j = issue
          if token.lower() == tokens[i][j].lower():
              returned_issues.append(issue)
      return returned_issues
  
imports: from typing import Any, Dict, List, Optional, Tuple
  import numpy
  import pandas
  from cleanlab.internal.token_classification_utils import color_sentence, get_sentence
label_desc: Use image node_id 3 for calling a global function with example usage: filter_by_token(token, issues, tokens) and returns: returned_issues