class_docstr: Provides instrumentation hooks for important autologging lifecycle events, including:
  
      - Calls to `mlflow.autolog()` APIs
      - Calls to patched APIs with associated termination states
        ("success" and "failure due to error")
      - Calls to original / underlying APIs made by patched function code with
        associated termination states ("success" and "failure due to error")
  
  Default implementations are included for each of these hooks, which emit corresponding
  DEBUG-level logging statements. Developers can provide their own hook implementations
  by subclassing `AutologgingEventLogger` and calling the static
  `AutologgingEventLogger.set_logger()` method to supply a new event logger instance.
  
  Callers fetch the configured logger via `AutologgingEventLogger.get_logger()`
  and invoke one or more hooks (e.g., `AutologgingEventLogger.get_logger().log_autolog_called()`).
docstr: Fetches the configured `AutologgingEventLogger` instance for logging.
  
  Returns:
      The instance of `AutologgingEventLogger` specified via `set_logger`
      (if configured) or the default implementation of `AutologgingEventLogger`
      (if a logger was not configured via `set_logger`).
code: def get_logger():
      """Fetches the configured `AutologgingEventLogger` instance for logging.
  
      Returns:
          The instance of `AutologgingEventLogger` specified via `set_logger`
          (if configured) or the default implementation of `AutologgingEventLogger`
          (if a logger was not configured via `set_logger`).
  
      """
      return (
          AutologgingEventLogger._event_logger
          or AutologgingEventLogger()
      )
  
file_path: mlflow/mlflow/utils/autologging_utils/events.py
imports: import warnings
  from mlflow.utils.autologging_utils import _logger
label_desc: Use image node_id 1 for calling the AutologgingEventLogger obj's underlying member method code with example usage: obj.get_logger() and returns: unknown