class_docstr: Client of an MLflow Tracking Server that creates and manages experiments and runs.
class_docstr_tok: ["Client","of","an","MLflow","Tracking","Server","that","creates","and","manages","experiments","and","runs","."]
docstr: Update a run with the specified ID to a new status or name.
  
  :param run_id: The ID of the Run to update.
  :param status: The new status of the run to set, if specified.
                 At least one of ``status`` or ``name`` should be specified.
  :param name: The new name of the run to set, if specified.
               At least one of ``name`` or ``status`` should be specified.
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code: def update_run(self, run_id, status=None, name=None):
      """
      Update a run with the specified ID to a new status or name.
  
      :param run_id: The ID of the Run to update.
      :param status: The new status of the run to set, if specified.
                     At least one of ``status`` or ``name`` should be specified.
      :param name: The new name of the run to set, if specified.
                   At least one of ``name`` or ``status`` should be specified.
      """
      # Exit early
      if status is None and name is None:
          return
  
      run = self.get_run(run_id)
      status = status or run.info.status
      self.store.update_run_info(
          run_id=run_id,
          run_status=RunStatus.from_string(status),
          end_time=run.info.end_time,
          run_name=name,
      )
  
imports: import os
  from collections import OrderedDict
  from itertools import zip_longest
  from typing import List, Optional
  from mlflow.entities import ExperimentTag, Metric, Param, RunStatus, RunTag, ViewType
  from mlflow.entities.dataset_input import DatasetInput
  from mlflow.exceptions import MlflowException
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE, ErrorCode
  from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository
  from mlflow.store.tracking import GET_METRIC_HISTORY_MAX_RESULTS, SEARCH_MAX_RESULTS_DEFAULT
  from mlflow.tracking._tracking_service import utils
  from mlflow.tracking.metric_value_conversion_utils import convert_metric_value_to_float_if_possible
  from mlflow.utils import chunk_list
  from mlflow.utils.async_logging.run_operations import RunOperations, get_combined_run_operations
  from mlflow.utils.mlflow_tags import MLFLOW_USER
  from mlflow.utils.string_utils import is_string_type
  from mlflow.utils.time import get_current_time_millis
  from mlflow.utils.uri import add_databricks_profile_info_to_artifact_uri
  from mlflow.utils.validation import MAX_ENTITIES_PER_BATCH, MAX_METRICS_PER_BATCH, MAX_PARAMS_TAGS_PER_BATCH, PARAM_VALIDATION_MSG, _validate_experiment_artifact_location, _validate_run_id
label_desc: Use image node_id 18 for calling the TrackingServiceClient obj's underlying member method code with example usage: obj.update_run(run_id, status, name) without return types