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: Called during the execution of a patched API associated with an autologging integration
  when the original / underlying API invocation terminates successfully. For example,
  when a patched implementation of `sklearn.linear_model.LogisticRegression.fit()` invokes the
  original / underlying implementation of `LogisticRegression.fit()`, then this function is
  called if the original / underlying implementation successfully completes.
  
  Args:
      session: The `AutologgingSession` associated with the patched API call.
      patch_obj: The object (class, module, etc) on which the original API was called.
      function_name: The name of the original API that was called.
      call_args: The positional arguments passed to the original API call.
      call_kwargs: The keyword arguments passed to the original API call.
code: def log_original_function_success(
      self, session, patch_obj, function_name, call_args, call_kwargs
  ):
      """Called during the execution of a patched API associated with an autologging integration
      when the original / underlying API invocation terminates successfully. For example,
      when a patched implementation of `sklearn.linear_model.LogisticRegression.fit()` invokes the
      original / underlying implementation of `LogisticRegression.fit()`, then this function is
      called if the original / underlying implementation successfully completes.
  
      Args:
          session: The `AutologgingSession` associated with the patched API call.
          patch_obj: The object (class, module, etc) on which the original API was called.
          function_name: The name of the original API that was called.
          call_args: The positional arguments passed to the original API call.
          call_kwargs: The keyword arguments passed to the original API call.
  
      """
      _logger.debug(
          "Original function invocation completed successfully during execution of patched API"
          " call '%s.%s' for %s autologging. Original function was invoked with with"
          " args '%s' and kwargs '%s'",
          patch_obj,
          function_name,
          session.integration,
          call_args,
          call_kwargs,
      )
  
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 8 for calling the AutologgingEventLogger obj's underlying member method code with example usage: obj.log_original_function_success(session, patch_obj, function_name, call_args, call_kwargs) without return types