class_docstr: Defines the base class RAIBaseInsights for the top-level API.
  
  This class is abstract and should not be instantiated.
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docstr: Creates an RAIBaseInsights object.
  
  :param model: The model to compute RAI insights for.
      A model that implements sklearn.predict or sklearn.predict_proba
      or function that accepts a 2d ndarray.
  :type model: object
  :param train: The training dataset including the label column.
      This parameter is optional as some extending downstream classes
      may not require it.
  :type train: pandas.DataFrame
  :param test: The test dataset including the label column.
      Note this parameter is always required.
  :type test: pandas.DataFrame
  :param target_column: The name of the label column.
  :type target_column: str
  :param task_type: The task to run.
  :type task_type: str
  :param classes: The class labels in the training dataset
  :type classes: numpy.ndarray
  :param serializer: Picklable custom serializer with save and load
      methods for custom model serialization.
      The save method writes the model to file given a parent directory.
      The load method returns the deserialized model from the same
      parent directory.
  :type serializer: object
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code: def __init__(
      self,
      model: Optional[Any],
      train: Optional[pd.DataFrame],
      test: pd.DataFrame,
      target_column: str,
      task_type: str,
      serializer: Optional[Any] = None,
  ):
      """Creates an RAIBaseInsights object.
  
      :param model: The model to compute RAI insights for.
          A model that implements sklearn.predict or sklearn.predict_proba
          or function that accepts a 2d ndarray.
      :type model: object
      :param train: The training dataset including the label column.
          This parameter is optional as some extending downstream classes
          may not require it.
      :type train: pandas.DataFrame
      :param test: The test dataset including the label column.
          Note this parameter is always required.
      :type test: pandas.DataFrame
      :param target_column: The name of the label column.
      :type target_column: str
      :param task_type: The task to run.
      :type task_type: str
      :param classes: The class labels in the training dataset
      :type classes: numpy.ndarray
      :param serializer: Picklable custom serializer with save and load
          methods for custom model serialization.
          The save method writes the model to file given a parent directory.
          The load method returns the deserialized model from the same
          parent directory.
      :type serializer: object
      """
      self.model = model
      self.train = train
      self.test = test
      self.target_column = target_column
      self.task_type = task_type
      self._serializer = serializer
  
imports: import json
  import os
  import pickle
  import warnings
  from abc import ABC, abstractmethod
  from pathlib import Path
  from typing import Any, Optional
  import pandas
  import responsibleai
  from raiutils.exceptions import UserConfigValidationException
  from responsibleai._internal._served_model_wrapper import ServedModelWrapper
  from responsibleai._internal.constants import FileFormats, Metadata, ModelServingConstants, SerializationAttributes
label_desc: Use image node_id 1 to create a new RAIBaseInsights object from inherited base classes: ABC with example: obj = RAIBaseInsights(model, train, test, target_column, task_type, serializer)