class_docstr: Callback for auto-logging metrics and parameters.
code: def __init__(
      self,
      client,
      metrics_logger,
      run_id,
      log_models,
      log_every_n_epoch,
  ):
      super().__init__()
      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.epoch = 0
  
file_path: mlflow/mlflow/paddle/_paddle_autolog.py
imports: import paddle
  import mlflow
  from mlflow.utils.autologging_utils import BatchMetricsLogger, ExceptionSafeAbstractClass, MlflowAutologgingQueueingClient, get_autologging_config
label_desc: Use image node_id 1 to create a new __MLflowPaddleCallback object from inherited base classes: paddle.callbacks with example: obj = __MLflowPaddleCallback(client, metrics_logger, run_id, log_models, log_every_n_epoch)