class_docstr:     The BLEU evaluation metric class.
  Parameters
  ----------
  n_grams: list[int]
      The BLEU's n_gram parameter. The results will be returned according             to the ``n_grams`` one-by-one.
  verbose: int, default = 0
      The log indicator. If set to 0, it will output no logs.
docstr:     The function to check the parameters.
      If all tests are passed, it will find the max value in ``n_grams``.
  Parameters
  ----------
  n_grams: list[int]
  
  Returns
  -------
  max_n_grams_value: int
docstr_tok: ["The","function","to","check","the","parameters",".","If","all","tests","are","passed",",","it","will","find","the","max","value","in","``","n_grams","``",".","Parameters","--","--","--","--","--","n_grams",":","list","[","int","]","Returns","--","--","--","-","max_n_grams_value",":","int"]
code: def _check_available(n_grams):
      """
          The function to check the parameters.
          If all tests are passed, it will find the max value in ``n_grams``.
      Parameters
      ----------
      n_grams: list[int]
  
      Returns
      -------
      max_n_grams_value: int
      """
      n_grams_ok = True
      if isinstance(n_grams, list):
          for i in n_grams:
              if not isinstance(i, int):
                  n_grams_ok = False
      else:
          n_grams_ok = False
      if not n_grams_ok:
          raise TypeError("argument n_grams must be in list of int.")
      return max(n_grams)
  
imports: from graph4nlp.pytorch.modules.evaluation.base import EvaluationMetricBase
  from graph4nlp.pytorch.modules.evaluation.bleu_tool.bleu import Bleu
label_desc: Use image node_id 3 for calling the BLEU obj's underlying member method code with example usage: obj._check_available(n_grams) and returns: max