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: Download an artifact file or directory from a run to a local directory if applicable,
  and return a local path for it.
  
  :param run_id: The run to download artifacts from.
  :param path: Relative source path to the desired artifact.
  :param dst_path: Absolute path of the local filesystem destination directory to which to
                   download the specified artifacts. This directory must already exist.
                   If unspecified, the artifacts will either be downloaded to a new
                   uniquely-named directory on the local filesystem or will be returned
                   directly in the case of the LocalArtifactRepository.
  :return: Local path of desired artifact.
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code: def download_artifacts(self, run_id, path, dst_path=None):
      """
      Download an artifact file or directory from a run to a local directory if applicable,
      and return a local path for it.
  
      :param run_id: The run to download artifacts from.
      :param path: Relative source path to the desired artifact.
      :param dst_path: Absolute path of the local filesystem destination directory to which to
                       download the specified artifacts. This directory must already exist.
                       If unspecified, the artifacts will either be downloaded to a new
                       uniquely-named directory on the local filesystem or will be returned
                       directly in the case of the LocalArtifactRepository.
      :return: Local path of desired artifact.
      """
      return self._get_artifact_repo(run_id).download_artifacts(
          path, dst_path
      )
  
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 26 for calling the TrackingServiceClient obj's underlying member method code with example usage: obj.download_artifacts(run_id, path, dst_path) and returns: self