class_docstr: Util class for setting up and logging to MLflow.
  
  Use this util for any library that needs MLflow logging/tracking logic
  such as Ray Tune or Ray Train.
class_docstr_tok: ["Util","class","for","setting","up","and","logging","to","MLflow",".","Use","this","util","for","any","library","that","needs","MLflow","logging\/tracking","logic","such","as","Ray","Tune","or","Ray","Train","."]
docstr: Returns an ml.tracking.MlflowClient instance to use for logging.
docstr_tok: ["Returns","an","ml.tracking.MlflowClient","instance","to","use","for","logging","."]
code: def _get_client(self) -> "MlflowClient":
      """Returns an ml.tracking.MlflowClient instance to use for logging."""
      tracking_uri = self._mlflow.get_tracking_uri()
      registry_uri = self._mlflow.get_registry_uri()
  
      from mlflow.tracking import MlflowClient
  
      return MlflowClient(
          tracking_uri=tracking_uri, registry_uri=registry_uri
      )
  
file_path: ray/python/ray/air/_internal/mlflow.py
imports: import logging
  import os
  from packaging import version
  from copy import deepcopy
  from typing import TYPE_CHECKING, Dict, Optional
  from ray._private.dict import flatten_dict
label_desc: Use image node_id 8 for calling the _MLflowLoggerUtil obj's underlying member method code with example usage: obj._get_client() and returns: MlflowClient