code: def get_default_scorers(model_type, class_avg: bool = True):
      """Get default scorers based on model type.
  
      Parameters
      ----------
      model_type : TaskType
          model type to return scorers for
      class_avg : bool, default True
          for classification whether to return scorers of average score or per class
      """
      return_array = (
          model_type in [TaskType.MULTICLASS, TaskType.BINARY]
          and class_avg is False
      )
  
      if return_array:
          return MULTICLASS_SCORERS_NON_AVERAGE
      else:
          return DEFAULT_SCORERS_DICT[model_type]
  
imports: import logging
  import typing
  import warnings
  from numbers import Number
  from typing import TYPE_CHECKING
  import numpy
  import pandas
  from packaging import version
  from sklearn import __version__
  from sklearn.base import ClassifierMixin
  from sklearn.metrics import get_scorer, log_loss, make_scorer, mean_absolute_error, mean_squared_error
  from sklearn.metrics._scorer import _BaseScorer, _ProbaScorer
  from sklearn.preprocessing import OneHotEncoder
  from deepchecks.core import errors
  from deepchecks.tabular.metric_utils.additional_classification_metrics import false_negative_rate_metric, false_positive_rate_metric, roc_auc_per_class, true_negative_rate_metric
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.docref import doclink
  from deepchecks.utils.logger import get_logger
  from deepchecks.utils.metrics import get_scorer_name
  from deepchecks.utils.simple_models import PerfectModel
  from deepchecks.utils.typing import BasicModel
  from deepchecks.utils.validation import is_sequence_not_str
label_desc: Use image node_id 1 for calling a global function with example usage: get_default_scorers(model_type, class_avg) and returns: MULTICLASS_SCORERS_NON_AVERAGE, DEFAULT_SCORERS_DICT