docstr: Ensure metrics lists input parameters are correct types and compatible with each other
docstr_tok: ["Ensure","metrics","lists","input","parameters","are","correct","types","and","compatible","with","each","other"]
code: def _validate_metrics_list_parameters(self):
      """Ensure metrics lists input parameters are correct types and compatible with each other"""
      for _d_type, _m_val in [
          (_, getattr(self, f"_ScoringMixIn{_}")) for _ in data_types
      ]:
          if _m_val == "all":
              setattr(
                  self,
                  f"_ScoringMixIn{_d_type}",
                  list(self.metrics.keys()),
              )
          elif not isinstance(_m_val, list):
              raise TypeError(
                  f"{_d_type} must be one of: ['all', None, <list>], not {_m_val}"
              )
          else:
              for _id in _m_val:
                  if not isinstance(_id, str):
                      raise TypeError(
                          f"{_d_type} values must be of type str. Received {_id}"
                      )
                  if _id not in self.metrics.keys():
                      raise KeyError(
                          f"{_d_type} values must be in metrics. '{_id}' is not"
                      )
  
imports: from collections import OrderedDict
  from contextlib import suppress
  import numpy
  import pandas
  from typing import Dict, Iterable, List, Tuple, Union
  from sklearn import metrics
  from sklearn.utils.multiclass import type_of_target, unique_labels
label_desc: Use image node_id 2 for calling the ScoringMixIn obj's underlying member method code with example usage: obj._validate_metrics_list_parameters() without return types