code: def setup_mlflow_logging(
      trainer: Engine,
      optimizers: Optional[
          Union[Optimizer, Dict[str, Optimizer]]
      ] = None,
      evaluators: Optional[Union[Engine, Dict[str, Engine]]] = None,
      log_every_iters: int = 100,
      **kwargs: Any,
  ) -> MLflowLogger:
      """Method to setup MLflow logging on trainer and a list of evaluators. Logged metrics are:
  
          - Training metrics, e.g. running average loss values
          - Learning rate(s)
          - Evaluation metrics
  
      Args:
          trainer: trainer engine
          optimizers: single or dictionary of
              torch optimizers. If a dictionary, keys are used as tags arguments for logging.
          evaluators: single or dictionary of evaluators. If a dictionary,
              keys are used as tags arguments for logging.
          log_every_iters: interval for loggers attached to iteration events. To log every iteration,
              value can be set to 1 or None.
          kwargs: optional keyword args to be passed to construct the logger.
  
      Returns:
          :class:`~ignite.contrib.handlers.mlflow_logger.MLflowLogger`
      """
      logger = MLflowLogger(**kwargs)
      _setup_logging(
          logger, trainer, optimizers, evaluators, log_every_iters
      )
      return logger
  
imports: import numbers
  import warnings
  from functools import partial
  from typing import Any, Callable, cast, Dict, Iterable, Mapping, Optional, Sequence, Union
  import torch
  import torch.nn
  from torch.optim.optimizer import Optimizer
  from torch.utils.data.distributed import DistributedSampler
  import ignite.distributed
  from ignite.contrib.handlers import ClearMLLogger, global_step_from_engine, MLflowLogger, NeptuneLogger, PolyaxonLogger, ProgressBar, TensorboardLogger, VisdomLogger, WandBLogger
  from ignite.contrib.handlers.base_logger import BaseLogger
  from ignite.contrib.metrics import GpuInfo
  from ignite.engine import Engine, Events
  from ignite.handlers import Checkpoint, DiskSaver, EarlyStopping, TerminateOnNan
  from ignite.handlers.checkpoint import BaseSaveHandler
  from ignite.handlers.param_scheduler import ParamScheduler
  from ignite.metrics import RunningAverage
  from ignite.metrics.metric import RunningBatchWise
  from ignite.utils import deprecated
label_desc: Use image node_id 9 for calling a global function with example usage: setup_mlflow_logging(trainer, optimizers, evaluators, log_every_iters) and returns: logger