code: def supports_partial_fit(estimator, params: dict = None) -> bool:
      # special case for MLP
      from sklearn.neural_network import MLPClassifier
  
      if isinstance(estimator, MLPClassifier):
          try:
              if (
                  params
                  and "solver" in params
                  and "lbfgs" in list(params["solver"])
              ) or estimator.solver == "lbfgs":
                  return False
          except Exception:
              return False
  
      if isinstance(estimator, Pipeline):
          return hasattr(estimator.steps[-1][1], "partial_fit")
  
      return hasattr(estimator, "partial_fit")
  
file_path: pycaret/pycaret/internal/validation.py
imports: from copy import deepcopy
  import pandas
  from sklearn.pipeline import Pipeline
  from sklearn.utils.validation import check_is_fitted
  from pycaret.internal.logging import get_logger
label_desc: Use image node_id 4 for calling a global function with example usage: supports_partial_fit(estimator, params) and returns: hasattr, hasattr, False, False