code: def __call__(self, X, y, logger, feat_type=None):
      if feat_type is None:
          feat_type = {i: "numerical" for i in range(X.shape[1])}
      starttime = time.time()
      try:
          if scipy.sparse.issparse(X) and hasattr(
              self, "_calculate_sparse"
          ):
              value = self._calculate_sparse(X, y, logger, feat_type)
          else:
              value = self._calculate(X, y, logger, feat_type)
          comment = ""
      except MemoryError:
          value = None
          comment = "Memory Error"
  
      endtime = time.time()
      return MetaFeatureValue(
          self.__class__.__name__,
          self.type_,
          0,
          0,
          value,
          endtime - starttime,
          comment=comment,
      )
  
imports: from abc import ABCMeta, abstractmethod
  import time
  from io import StringIO
  import arff
  import scipy.sparse
label_desc: Use image node_id 3 for calling the AbstractMetaFeature obj's underlying member method code with example usage: obj.__call__(X, y, logger, feat_type) and returns: MetaFeatureValue