code: def custom_log_callback(output, execution_time, **kwargs):
      """My custom `log_callback` function
  
      Parameters
      ----------
      output : tuple(
              numpy.ndarray or pandas.DataFrame
              :class:estimator or :class:transformer
          )
          The output of the step and a step in the pipeline.
      execution_time : float
          The execution time of the step.
      """
      logger = logging.getLogger(__name__)
      step_result, step = output
      logger.info(
          f"[{step}] shape={step_result.shape} "
          f"nbytes={step_result.nbytes} time={execution_time}"
      )
  
imports: import logging
  import pytest
  from sklearn import datasets
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.model_selection import GridSearchCV
  from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier, OutputCodeClassifier
  from sklearn.pipeline import FeatureUnion
  from sklearn.preprocessing import StandardScaler
  from sklearn.svm import LinearSVC
  from sklego.pipeline import DebugPipeline, make_debug_pipeline
label_desc: Use image node_id 1 for calling a global function with example usage: custom_log_callback(output, execution_time) without return types