code: def _log_specialized_estimator_content(
      autologging_client,
      fitted_estimator,
      run_id,
      prefix,
      X,
      y_true,
      sample_weight,
      pos_label,
  ):
      import sklearn
  
      metrics = {}
  
      if y_true is not None:
          try:
              if sklearn.base.is_classifier(fitted_estimator):
                  metrics = _get_classifier_metrics(
                      fitted_estimator,
                      prefix,
                      X,
                      y_true,
                      sample_weight,
                      pos_label,
                  )
              elif sklearn.base.is_regressor(fitted_estimator):
                  metrics = _get_regressor_metrics(
                      fitted_estimator, prefix, X, y_true, sample_weight
                  )
          except Exception as err:
              msg = (
                  "Failed to autolog metrics for "
                  + fitted_estimator.__class__.__name__
                  + ". Logging error: "
                  + str(err)
              )
              _logger.warning(msg)
          else:
              autologging_client.log_metrics(
                  run_id=run_id, metrics=metrics
              )
  
      if sklearn.base.is_classifier(fitted_estimator):
          try:
              artifacts = _get_classifier_artifacts(
                  fitted_estimator, prefix, X, y_true, sample_weight
              )
          except Exception as e:
              msg = (
                  "Failed to autolog artifacts for "
                  + fitted_estimator.__class__.__name__
                  + ". Logging error: "
                  + str(e)
              )
              _logger.warning(msg)
              return metrics
  
          try:
              import matplotlib
              import matplotlib.pyplot as plt
          except ImportError as ie:
              _logger.warning(
                  f"Failed to import matplotlib (error: {ie!r}). Skipping artifact logging."
              )
              return metrics
  
          _matplotlib_config = {
              "savefig.dpi": 175,
              "figure.autolayout": True,
              "font.size": 8,
          }
          with TempDir() as tmp_dir:
              for artifact in artifacts:
                  try:
                      with matplotlib.rc_context(_matplotlib_config):
                          display = artifact.function(
                              **artifact.arguments
                          )
                          display.ax_.set_title(artifact.title)
                          artifact_path = f"{artifact.name}.png"
                          filepath = tmp_dir.path(artifact_path)
                          display.figure_.savefig(
                              fname=filepath, format="png"
                          )
                          plt.close(display.figure_)
                  except Exception as e:
                      _log_warning_for_artifacts(
                          artifact.name, artifact.function, e
                      )
  
              MlflowClient().log_artifacts(run_id, tmp_dir.path())
  
      return metrics
  
imports: import collections
  import inspect
  import logging
  import pkgutil
  import platform
  import warnings
  from copy import deepcopy
  from importlib import import_module
  from numbers import Number
  from operator import itemgetter
  import numpy
  from packaging.version import Version
  from mlflow import MlflowClient
  from mlflow.utils.arguments_utils import _get_arg_names
  from mlflow.utils.file_utils import TempDir
  from mlflow.utils.mlflow_tags import MLFLOW_PARENT_RUN_ID
  from mlflow.utils.time import get_current_time_millis
label_desc: Use image node_id 12 for calling a global function with example usage: _log_specialized_estimator_content(autologging_client, fitted_estimator, run_id, prefix, X, y_true, sample_weight, pos_label) and returns: metrics, metrics, metrics