class_docstr: Log using `MLflow <https://mlflow.org>`_.
  
  Install it with pip:
  
  .. code-block:: bash
  
      pip install mlflow  # or mlflow-skinny
  
  .. code-block:: python
  
      from lightning.pytorch import Trainer
      from lightning.pytorch.loggers import MLFlowLogger
  
      mlf_logger = MLFlowLogger(experiment_name="lightning_logs", tracking_uri="file:./ml-runs")
      trainer = Trainer(logger=mlf_logger)
  
  Use the logger anywhere in your :class:`~lightning.pytorch.core.LightningModule` as follows:
  
  .. code-block:: python
  
      from lightning.pytorch import LightningModule
  
  
      class LitModel(LightningModule):
          def training_step(self, batch, batch_idx):
              # example
              self.logger.experiment.whatever_ml_flow_supports(...)
  
          def any_lightning_module_function_or_hook(self):
              self.logger.experiment.whatever_ml_flow_supports(...)
  
  Args:
      experiment_name: The name of the experiment.
      run_name: Name of the new run. The `run_name` is internally stored as a ``mlflow.runName`` tag.
          If the ``mlflow.runName`` tag has already been set in `tags`, the value is overridden by the `run_name`.
      tracking_uri: Address of local or remote tracking server.
          If not provided, defaults to `MLFLOW_TRACKING_URI` environment variable if set, otherwise it falls
          back to `file:<save_dir>`.
      tags: A dictionary tags for the experiment.
      save_dir: A path to a local directory where the MLflow runs get saved.
          Defaults to `./mlruns` if `tracking_uri` is not provided.
          Has no effect if `tracking_uri` is provided.
      log_model: Log checkpoints created by :class:`~lightning.pytorch.callbacks.model_checkpoint.ModelCheckpoint`
          as MLFlow artifacts.
  
          * if ``log_model == 'all'``, checkpoints are logged during training.
          * if ``log_model == True``, checkpoints are logged at the end of training, except when
            :paramref:`~lightning.pytorch.callbacks.Checkpoint.save_top_k` ``== -1``
            which also logs every checkpoint during training.
          * if ``log_model == False`` (default), no checkpoint is logged.
  
      prefix: A string to put at the beginning of metric keys.
      artifact_location: The location to store run artifacts. If not provided, the server picks an appropriate
          default.
      run_id: The run identifier of the experiment. If not provided, a new run is started.
  
  Raises:
      ModuleNotFoundError:
          If required MLFlow package is not installed on the device.
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code: def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:  # type: ignore[override]
      params = _convert_params(params)
      params = _flatten_dict(params)
  
      from mlflow.entities import Param
  
      # Truncate parameter values to 250 characters.
      # TODO: MLflow 1.28 allows up to 500 characters: https://github.com/mlflow/mlflow/releases/tag/v1.28.0
      params_list = [
          Param(key=k, value=str(v)[:250]) for k, v in params.items()
      ]
  
      # Log in chunks of 100 parameters (the maximum allowed by MLflow).
      for idx in range(0, len(params_list), 100):
          self.experiment.log_batch(
              run_id=self.run_id, params=params_list[idx : idx + 100]
          )
  
imports: import logging
  import os
  import re
  import tempfile
  from argparse import Namespace
  from pathlib import Path
  from time import time
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Mapping, Optional, Union
  import yaml
  from lightning_utilities.core.imports import RequirementCache
  from torch import Tensor
  from typing_extensions import override
  from lightning.fabric.utilities.logger import _add_prefix, _convert_params, _flatten_dict
  from lightning.pytorch.callbacks.model_checkpoint import ModelCheckpoint
  from lightning.pytorch.loggers.logger import Logger, rank_zero_experiment
  from lightning.pytorch.loggers.utilities import _scan_checkpoints
  from lightning.pytorch.utilities.rank_zero import rank_zero_only, rank_zero_warn
label_desc: Use image node_id 5 for calling the MLFlowLogger obj's underlying member method code with example usage: obj.log_hyperparams(params) without return types