code: def autologging_integration(name):
      """
      **All autologging integrations should be decorated with this wrapper.**
  
      Wraps an autologging function in order to store its configuration arguments. This enables
      patch functions to broadly obey certain configurations (e.g., disable=True) without
      requiring specific logic to be present in each autologging integration.
      """
  
      def validate_param_spec(param_spec):
          if (
              "disable" not in param_spec
              or param_spec["disable"].default is not False
          ):
              raise Exception(
                  f"Invalid `autolog()` function for integration '{name}'. `autolog()` functions"
                  " must specify a 'disable' argument with default value 'False'"
              )
          elif (
              "silent" not in param_spec
              or param_spec["silent"].default is not False
          ):
              raise Exception(
                  f"Invalid `autolog()` function for integration '{name}'. `autolog()` functions"
                  " must specify a 'silent' argument with default value 'False'"
              )
  
      def wrapper(_autolog):
          param_spec = inspect.signature(_autolog).parameters
          validate_param_spec(param_spec)
  
          AUTOLOGGING_INTEGRATIONS[name] = {}
          default_params = {
              param.name: param.default for param in param_spec.values()
          }
  
          def autolog(*args, **kwargs):
              config_to_store = dict(default_params)
              config_to_store.update(
                  {
                      param.name: arg
                      for arg, param in zip(args, param_spec.values())
                  }
              )
              config_to_store.update(kwargs)
              AUTOLOGGING_INTEGRATIONS[name] = config_to_store
  
              try:
                  # Pass `autolog()` arguments to `log_autolog_called` in keyword format to enable
                  # event loggers to more easily identify important configuration parameters
                  # (e.g., `disable`) without examining positional arguments. Passing positional
                  # arguments to `log_autolog_called` is deprecated in MLflow > 1.13.1
                  AutologgingEventLogger.get_logger().log_autolog_called(
                      name, (), config_to_store
                  )
              except Exception:
                  pass
  
              revert_patches(name)
  
              # If disabling autologging using fluent api, then every active integration's autolog
              # needs to be called with disable=True. So do not short circuit and let
              # `mlflow.autolog()` invoke all active integrations with disable=True.
              if name != "mlflow" and get_autologging_config(
                  name, "disable", True
              ):
                  return
  
              is_silent_mode = get_autologging_config(
                  name, "silent", False
              )
              # Reroute non-MLflow warnings encountered during autologging enablement to an
              # MLflow event logger, and enforce silent mode if applicable (i.e. if the corresponding
              # autologging integration was called with `silent=True`)
              with set_mlflow_events_and_warnings_behavior_globally(
                  # MLflow warnings emitted during autologging setup / enablement are likely
                  # actionable and relevant to the user, so they should be emitted as normal
                  # when `silent=False`. For reference, see recommended warning and event logging
                  # behaviors from https://docs.python.org/3/howto/logging.html#when-to-use-logging
                  reroute_warnings=False,
                  disable_event_logs=is_silent_mode,
                  disable_warnings=is_silent_mode,
              ), set_non_mlflow_warnings_behavior_for_current_thread(
                  # non-MLflow warnings emitted during autologging setup / enablement are not
                  # actionable for the user, as they are a byproduct of the autologging
                  # implementation. Accordingly, they should be rerouted to `logger.warning()`.
                  # For reference, see recommended warning and event logging
                  # behaviors from https://docs.python.org/3/howto/logging.html#when-to-use-logging
                  reroute_warnings=True,
                  disable_warnings=is_silent_mode,
              ):
                  _check_and_log_warning_for_unsupported_package_versions(
                      name
                  )
  
                  return _autolog(*args, **kwargs)
  
          wrapped_autolog = update_wrapper_extended(autolog, _autolog)
          # Set the autologging integration name as a function attribute on the wrapped autologging
          # function, allowing the integration name to be extracted from the function. This is used
          # during the execution of import hooks for `mlflow.autolog()`.
          wrapped_autolog.integration_name = name
  
          if name in FLAVOR_TO_MODULE_NAME_AND_VERSION_INFO_KEY:
              wrapped_autolog.__doc__ = (
                  gen_autologging_package_version_requirements_doc(name)
                  + (wrapped_autolog.__doc__ or "")
              )
          return wrapped_autolog
  
      return wrapper
  
imports: import contextlib
  import inspect
  import logging
  import time
  from typing import List
  import mlflow
  from mlflow.entities import Metric
  from mlflow.tracking.client import MlflowClient
  from mlflow.utils.validation import MAX_METRICS_PER_BATCH
  from mlflow.utils.autologging_utils.client import MlflowAutologgingQueueingClient
  from mlflow.utils.autologging_utils.events import AutologgingEventLogger
  from mlflow.utils.autologging_utils.logging_and_warnings import set_mlflow_events_and_warnings_behavior_globally, set_non_mlflow_warnings_behavior_for_current_thread
  from mlflow.utils.autologging_utils.safety import ExceptionSafeAbstractClass, ExceptionSafeClass, PatchFunction, exception_safe_function_for_class, is_testing, picklable_exception_safe_function, revert_patches, safe_patch, update_wrapper_extended, with_managed_run
  from mlflow.utils.autologging_utils.versioning import FLAVOR_TO_MODULE_NAME_AND_VERSION_INFO_KEY, get_min_max_version_and_pip_release, is_flavor_supported_for_associated_package_versions
label_desc: Use image node_id 7 for calling a global function with example usage: autologging_integration(name) and returns: wrapper, wrapped_autolog, _autolog