class_docstr:     Calculate precision, recall, F1 for each labels
  
  Parameters
  ----------
  metrics: list
      Indicate the metric for the class to return.
      Note that each metric must be one of ``precision``, ``recall``, ``F1``, ``accuracy``.
      And the results' order is the same as the metrics.
class_docstr_tok: ["Calculate","precision",",","recall",",","F1","for","each","labels","Parameters","--","--","--","--","--","metrics",":","list","Indicate","the","metric","for","the","class","to","return",".","Note","that","each","metric","must","be","one","of","``","precision","``",",","``","recall","``",",","``","F1","``",",","``","accuracy","``",".","And","the","results","'","order","is","the","same","as","the","metrics","."]
docstr:     find the unique elements in the given lists
  
  Parameters
  ----------
  lists: [numpy.ndarray]
      List of lists which contain labels.
  Returns
  -------
  unique_labels: numpy.ndarray
      It has unique labels encountered in the ``lists``.
docstr_tok: ["find","the","unique","elements","in","the","given","lists","Parameters","--","--","--","--","--","lists",":","[","numpy.ndarray","]","List","of","lists","which","contain","labels",".","Returns","--","--","--","-","unique_labels",":","numpy.ndarray","It","has","unique","labels","encountered","in","the","``","lists","``","."]
code: def _get_unique_labels(*lists):
      """
          find the unique elements in the given lists
  
      Parameters
      ----------
      lists: [numpy.ndarray]
          List of lists which contain labels.
      Returns
      -------
      unique_labels: numpy.ndarray
          It has unique labels encountered in the ``lists``.
      """
      ret = []
      for li in lists:
          unique_li = np.unique(li)
          ret.extend(unique_li.tolist())
      ret = list(set(ret))
      return np.array(ret)
  
file_path: graph4nlp/graph4nlp/pytorch/modules/evaluation/accuracy.py
imports: import copy
  import warnings
  import numpy
  import torch
  from graph4nlp.pytorch.modules.evaluation.base import EvaluationMetricBase
label_desc: Use image node_id 6 for calling the Accuracy obj's underlying member method code with example usage: obj._get_unique_labels() and returns: np