code: def predict(target, name, input_path, output_path, endpoint):
      """
      Predict the results for the deployed model for the given input(s)
      """
      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.predict)
      if "endpoint" in sig.parameters:
          result = client.predict(name, df, endpoint=endpoint)
      else:
          result = client.predict(name, df)
      if output_path is not None:
          result.to_json(output_path)
      else:
          click.echo(result.to_json())
  
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 11 for calling a global function with example usage: predict(target, name, input_path, output_path, endpoint) without return types