code: def _transform_one(transformer, X=None, y=None):
      """Transform the data using one transformer."""
      args = []
      if "X" in signature(transformer.transform).parameters:
          args.append(X)
      if "y" in signature(transformer.transform).parameters:
          args.append(y)
      output = transformer.transform(*args)
  
      if isinstance(output, tuple):
          X, y = output[0], output[1]
      else:
          if len(output.shape) > 1:
              X, y = output, y  # Only X
          else:
              X, y = X, output  # Only y
  
      return X, y
  
imports: import platform
  import warnings
  from copy import deepcopy
  from inspect import signature
  import imblearn.pipeline
  import sklearn.pipeline
  from sklearn.base import clone
  from sklearn.utils import _print_elapsed_time
  from sklearn.utils.metaestimators import available_if
  from sklearn.utils.validation import check_memory
  from pycaret.utils._show_versions import _get_deps_info
  from pycaret.utils.generic import get_all_object_vars_and_properties, variable_return
label_desc: Use image node_id 4 for calling a global function with example usage: _transform_one(transformer, X, y) and returns: X,  y