code: def approximate_rbf_kernel_features(
      X: np.ndarray,
      num_random_components: int,
      precision: Optional[float] = None,
  ) -> np.ndarray:
      """Applies the Nystroem method to create a NxD (D << N) approximated RBF kernel map using a subset of the data,
      where N is the number of samples in X and D the number of components.
  
      :param X: Input data.
      :param num_random_components: Number of components D for the approximated kernel map.
      :param precision: Specific precision matrix for the RBF kernel. If None is given, this is inferred from the data.
      :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)
  
      if precision is None:
          precision = _median_based_precision(
              euclidean_distances(X, squared=True)
          )
  
      return Nystroem(
          kernel="rbf",
          gamma=precision,
          n_components=num_random_components,
      ).fit_transform(X)
  
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 4 for calling a global function with example usage: approximate_rbf_kernel_features(X, num_random_components, precision) and returns: Nystroem