code: def explain(target, name, input_path, output_path, endpoint):
      """
      Generate explanations of model predictions on the specified input for
      the deployed model for the given input(s). Explanation output formats vary
      by deployment target, and can include details like feature importance for
      understanding/debugging predictions. Run `mlflow deployments help` or
      consult the documentation for your plugin for details on explanation format.
      For information about the input data formats accepted by this function,
      see the following documentation:
      https://www.mlflow.org/docs/latest/models.html#built-in-deployment-tools
      """
      import pandas as pd
  
      if (name, endpoint).count(None) != 1:
          raise click.UsageError(
              "Must specify exactly one of --name or --endpoint."
          )
  
      df = pd.read_json(input_path)
      client = interface.get_deploy_client(target)
  
      sig = signature(client.explain)
      if "endpoint" in sig.parameters:
          result = client.explain(name, df, endpoint=endpoint)
      else:
          result = client.explain(name, df)
      if output_path:
          with open(output_path, "w") as fp:
              predictions_to_json(result, fp)
      else:
          predictions_to_json(result, sys.stdout)
  
imports: import json
  import sys
  from inspect import signature
  import click
  from mlflow.deployments import interface
  from mlflow.environment_variables import MLFLOW_DEPLOYMENTS_CONFIG
  from mlflow.utils import cli_args
  from mlflow.utils.annotations import experimental
  from mlflow.utils.proto_json_utils import NumpyEncoder, _get_jsonable_obj
label_desc: Use image node_id 12 for calling a global function with example usage: explain(target, name, input_path, output_path, endpoint) without return types