class_docstr: Base class exposing Python model deployment APIs.
  
  Plugin implementors should define target-specific deployment logic via a subclass of
  ``BaseDeploymentClient`` within the plugin module, and customize method docstrings with
  target-specific information.
  
  .. Note::
      Subclasses should raise :py:class:`mlflow.exceptions.MlflowException` in error cases (e.g.
      on failure to deploy a model).
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docstr: Generate explanations of model predictions on the specified input pandas Dataframe
  ``df`` for the deployed model. Explanation output formats vary by deployment target,
  and can include details like feature importance for understanding/debugging predictions.
  
  Args:
      deployment_name: Name of deployment to predict against
      df: Pandas DataFrame to use for explaining feature importance in model prediction
      endpoint: Endpoint to predict against. May not be supported by all targets
  
  Returns:
      A JSON-able object (pandas dataframe, numpy array, dictionary), or
      an exception if the implementation is not available in deployment target's class
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code: def explain(
      self, deployment_name=None, df=None, endpoint=None
  ):  # pylint: disable=unused-argument
      """
      Generate explanations of model predictions on the specified input pandas Dataframe
      ``df`` for the deployed model. Explanation output formats vary by deployment target,
      and can include details like feature importance for understanding/debugging predictions.
  
      Args:
          deployment_name: Name of deployment to predict against
          df: Pandas DataFrame to use for explaining feature importance in model prediction
          endpoint: Endpoint to predict against. May not be supported by all targets
  
      Returns:
          A JSON-able object (pandas dataframe, numpy array, dictionary), or
          an exception if the implementation is not available in deployment target's class
      """
      raise MlflowException(
          "Computing model explanations is not yet supported for this deployment target"
      )
  
file_path: mlflow/mlflow/deployments/base.py
imports: import abc
  from mlflow.exceptions import MlflowException
  from mlflow.utils.annotations import developer_stable
label_desc: Use image node_id 8 for calling the BaseDeploymentClient obj's underlying member method code with example usage: obj.explain(deployment_name, df, endpoint) without return types