class_docstr: Callback for auto-logging metrics and parameters.
class_docstr_tok: ["Callback","for","auto-logging","metrics","and","parameters","."]
code: def __init__(
      self,
      client,
      metrics_logger,
      run_id,
      log_models,
      log_every_n_epoch,
      log_every_n_step,
  ):
      if log_every_n_step and _pl_version < Version("1.1.0"):
          raise MlflowException(
              "log_every_n_step is only supported for PyTorch-Lightning >= 1.1.0"
          )
      self.early_stopping = False
      self.client = client
      self.metrics_logger = metrics_logger
      self.run_id = run_id
      self.log_models = log_models
      self.log_every_n_epoch = log_every_n_epoch
      self.log_every_n_step = log_every_n_step
      self._global_steps_per_training_step = 1
      # Sets for tracking which metrics are logged on steps and which are logged on epochs
      self._step_metrics = set()
      self._epoch_metrics = set()
  
imports: import logging
  import os
  import tempfile
  import warnings
  from packaging.version import Version
  import mlflow.pytorch
  from mlflow.exceptions import MlflowException
  from mlflow.ml_package_versions import _ML_PACKAGE_VERSIONS
  from mlflow.pytorch import _pytorch_autolog
  from mlflow.utils.autologging_utils import BatchMetricsLogger, ExceptionSafeAbstractClass, MlflowAutologgingQueueingClient, get_autologging_config
  import pytorch_lightning
  from pytorch_lightning.utilities import rank_zero_only
label_desc: Use image node_id 1 to create a new __MLflowPLCallback object from inherited base classes: pl with example: obj = __MLflowPLCallback(client, metrics_logger, run_id, log_models, log_every_n_epoch, log_every_n_step)