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: Create a :py:class:`mlflow.entities.Run` object that can be associated with
  metrics, parameters, artifacts, etc.
  Unlike :py:func:`mlflow.projects.run`, creates objects but does not run code.
  Unlike :py:func:`mlflow.start_run`, does not change the "active run" used by
  :py:func:`mlflow.log_param`.
  
  :param experiment_id: The ID of the experiment to create a run in.
  :param start_time: If not provided, use the current timestamp.
  :param tags: A dictionary of key-value pairs that are converted into
               :py:class:`mlflow.entities.RunTag` objects.
  :param run_name: The name of this run.
  :return: :py:class:`mlflow.entities.Run` that was created.
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code: def create_run(
      self, experiment_id, start_time=None, tags=None, run_name=None
  ):
      """
      Create a :py:class:`mlflow.entities.Run` object that can be associated with
      metrics, parameters, artifacts, etc.
      Unlike :py:func:`mlflow.projects.run`, creates objects but does not run code.
      Unlike :py:func:`mlflow.start_run`, does not change the "active run" used by
      :py:func:`mlflow.log_param`.
  
      :param experiment_id: The ID of the experiment to create a run in.
      :param start_time: If not provided, use the current timestamp.
      :param tags: A dictionary of key-value pairs that are converted into
                   :py:class:`mlflow.entities.RunTag` objects.
      :param run_name: The name of this run.
      :return: :py:class:`mlflow.entities.Run` that was created.
      """
  
      tags = tags if tags else {}
  
      # Extract user from tags
      # This logic is temporary; the user_id attribute of runs is deprecated and will be removed
      # in a later release.
      user_id = tags.get(MLFLOW_USER, "unknown")
  
      return self.store.create_run(
          experiment_id=experiment_id,
          user_id=user_id,
          start_time=start_time or get_current_time_millis(),
          tags=[RunTag(key, value) for (key, value) in tags.items()],
          run_name=run_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 5 for calling the TrackingServiceClient obj's underlying member method code with example usage: obj.create_run(experiment_id, start_time, tags, run_name) and returns: self