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: Parses provided dict to convert all values to float.
  
  MLflow can only log metrics that are floats. This does not apply to
  logging parameters or artifacts.
  
  Args:
      dict_to_log: The dictionary containing the metrics to log.
  
  Returns:
      A dictionary containing the metrics to log with all values being
          converted to floats, or skipped if not able to be converted.
docstr_tok: ["Parses","provided","dict","to","convert","all","values","to","float",".","MLflow","can","only","log","metrics","that","are","floats",".","This","does","not","apply","to","logging","parameters","or","artifacts",".","Args",":","dict_to_log",":","The","dictionary","containing","the","metrics","to","log",".","Returns",":","A","dictionary","containing","the","metrics","to","log","with","all","values","being","converted","to","floats",",","or","skipped","if","not","able","to","be","converted","."]
code: def _parse_dict(self, dict_to_log: Dict) -> Dict:
      """Parses provided dict to convert all values to float.
  
      MLflow can only log metrics that are floats. This does not apply to
      logging parameters or artifacts.
  
      Args:
          dict_to_log: The dictionary containing the metrics to log.
  
      Returns:
          A dictionary containing the metrics to log with all values being
              converted to floats, or skipped if not able to be converted.
      """
      new_dict = {}
      for key, value in dict_to_log.items():
          try:
              value = float(value)
              new_dict[key] = value
          except (ValueError, TypeError):
              logger.debug(
                  "Cannot log key {} with value {} since the "
                  "value cannot be converted to float.".format(
                      key, value
                  )
              )
              continue
  
      return new_dict
  
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 4 for calling the _MLflowLoggerUtil obj's underlying member method code with example usage: obj._parse_dict(dict_to_log) and returns: new_dict