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 an experiment.
  
  :param name: The experiment name. Must be unique.
  :param artifact_location: The location to store run artifacts.
                            If not provided, the server picks an appropriate default.
  :param tags: A dictionary of key-value pairs that are converted into
                            :py:class:`mlflow.entities.ExperimentTag` objects.
  :return: Integer ID of the created experiment.
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code: def create_experiment(self, name, artifact_location=None, tags=None):
      """Create an experiment.
  
      :param name: The experiment name. Must be unique.
      :param artifact_location: The location to store run artifacts.
                                If not provided, the server picks an appropriate default.
      :param tags: A dictionary of key-value pairs that are converted into
                                :py:class:`mlflow.entities.ExperimentTag` objects.
      :return: Integer ID of the created experiment.
      """
      _validate_experiment_artifact_location(artifact_location)
  
      return self.store.create_experiment(
          name=name,
          artifact_location=artifact_location,
          tags=[
              ExperimentTag(key, value) for (key, value) in tags.items()
          ]
          if tags
          else [],
      )
  
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 9 for calling the TrackingServiceClient obj's underlying member method code with example usage: obj.create_experiment(name, artifact_location, tags) and returns: self