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: Fetch the run from backend store. The resulting :py:class:`Run <mlflow.entities.Run>`
  contains a collection of run metadata -- :py:class:`RunInfo <mlflow.entities.RunInfo>`,
  as well as a collection of run parameters, tags, and metrics --
  :py:class:`RunData <mlflow.entities.RunData>`. In the case where multiple metrics with the
  same key are logged for the run, the :py:class:`RunData <mlflow.entities.RunData>` contains
  the most recently logged value at the largest step for each metric.
  
  :param run_id: Unique identifier for the run.
  
  :return: A single :py:class:`mlflow.entities.Run` object, if the run exists. Otherwise,
           raises an exception.
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code: def get_run(self, run_id):
      """
      Fetch the run from backend store. The resulting :py:class:`Run <mlflow.entities.Run>`
      contains a collection of run metadata -- :py:class:`RunInfo <mlflow.entities.RunInfo>`,
      as well as a collection of run parameters, tags, and metrics --
      :py:class:`RunData <mlflow.entities.RunData>`. In the case where multiple metrics with the
      same key are logged for the run, the :py:class:`RunData <mlflow.entities.RunData>` contains
      the most recently logged value at the largest step for each metric.
  
      :param run_id: Unique identifier for the run.
  
      :return: A single :py:class:`mlflow.entities.Run` object, if the run exists. Otherwise,
               raises an exception.
      """
      _validate_run_id(run_id)
      return self.store.get_run(run_id)
  
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 3 for calling the TrackingServiceClient obj's underlying member method code with example usage: obj.get_run(run_id) and returns: self