code: def answer_relevance(
      model: Optional[str] = None,
      metric_version: Optional[str] = _get_latest_metric_version(),
      examples: Optional[List[EvaluationExample]] = None,
  ) -> EvaluationMetric:
      """
      This function will create a genai metric used to evaluate the answer relevance of an LLM
      using the model provided. Answer relevance will be assessed based on the appropriateness and
      applicability of the output with respect to the input.
  
      An MlflowException will be raised if the specified version for this metric does not exist.
  
      :param model: (Optional) Model uri of an openai or gateway judge model in the format of
          "openai:/gpt-4" or "gateway:/my-route". Defaults to
          "openai:/gpt-4". Your use of a third party LLM service (e.g., OpenAI) for
          evaluation may be subject to and governed by the LLM service's terms of use.
      :param metric_version: (Optional) The version of the answer relevance metric to use.
          Defaults to the latest version.
      :param examples: (Optional) Provide a list of examples to help the judge model evaluate the
          answer relevance. It is highly recommended to add examples to be used as a reference to
          evaluate the new results.
      :return: A metric object
      """
      class_name = f"mlflow.metrics.genai.prompts.{metric_version}.AnswerRelevanceMetric"
      try:
          answer_relevance_class_module = _get_class_from_string(
              class_name
          )
      except ModuleNotFoundError:
          raise MlflowException(
              f"Failed to find answer relevance metric for version {metric_version}."
              f" Please check the version",
              error_code=INVALID_PARAMETER_VALUE,
          ) from None
      except Exception as e:
          raise MlflowException(
              f"Failed to construct answer relevance metric {metric_version}. Error: {e!r}",
              error_code=INTERNAL_ERROR,
          ) from None
  
      if examples is None:
          examples = answer_relevance_class_module.default_examples
      if model is None:
          model = answer_relevance_class_module.default_model
  
      return make_genai_metric(
          name="answer_relevance",
          definition=answer_relevance_class_module.definition,
          grading_prompt=answer_relevance_class_module.grading_prompt,
          examples=examples,
          version=metric_version,
          model=model,
          parameters=answer_relevance_class_module.parameters,
          aggregations=["mean", "variance", "p90"],
          greater_is_better=True,
      )
  
file_path: mlflow/mlflow/metrics/genai/metric_definitions.py
imports: from typing import List, Optional
  from mlflow.exceptions import MlflowException
  from mlflow.metrics.genai.base import EvaluationExample
  from mlflow.metrics.genai.genai_metric import make_genai_metric
  from mlflow.metrics.genai.utils import _get_latest_metric_version
  from mlflow.models import EvaluationMetric
  from mlflow.protos.databricks_pb2 import INTERNAL_ERROR, INVALID_PARAMETER_VALUE
  from mlflow.utils.annotations import experimental
  from mlflow.utils.class_utils import _get_class_from_string
label_desc: Use image node_id 4 for calling a global function with example usage: answer_relevance(model, metric_version, examples) and returns: make_genai_metric