code: def approximate_delta_kernel_features(
      X: np.ndarray, num_random_components: int
  ) -> np.ndarray:
      """Applies the Nystroem method to create a NxD (D << N) approximated delta kernel map using a subset of the data,
      where N is the number of samples in X and D the number of components. The delta kernel gives 1 if two entries are
      equal and 0 otherwise.
  
      :param X: Input data.
      :param num_random_components: Number of components D for the approximated kernel map.
      :return: A NxD approximated RBF kernel map, where N is the number of samples in X and D the number of components.
      """
      X = shape_into_2d(X)
  
      def delta_function(x, y) -> float:
          return float(x == y)
  
      for i, unique_element in enumerate(np.unique(X)):
          X[X == unique_element] = i
  
      result = Nystroem(
          kernel=delta_function, n_components=num_random_components
      ).fit_transform(X.astype(int))
      result[result != 0] = 1
  
      return result
  
file_path: dowhy/dowhy/gcm/independence_test/kernel_operation.py
imports: from typing import Optional
  import numpy
  from sklearn.kernel_approximation import Nystroem
  from sklearn.metrics import euclidean_distances
  from dowhy.gcm.util.general import shape_into_2d
label_desc: Use image node_id 5 for calling a global function with example usage: approximate_delta_kernel_features(X, num_random_components) and returns: result, float