code: def hadamard_low_rank(K, R):
      """Efficient Hadamard product of two low-rank matrices.
  
      efficient multiplication of sum of low-rank representations
      using this and then get_low_rank_matrix is more efficient than an
      explicit computation if the rank of the final matrix is not full
  
      Parameters
      ----------
      K, R : LRdec type objects to add
  
      Returns
      -------
      LRdec type object for Hadamard product of K and R
      """
      rankK = K.U.shape[1]
      rankR = R.U.shape[1]
      U = np.tile(K.U, rankR) * np.repeat(R.U, rankK, 1)
      V = np.tile(K.V, rankR) * np.repeat(R.V, rankK, 1)
      return LRdec(U, V)
  
imports: import collections
  from functools import partial
  import numpy
  from scipy.sparse.linalg import svds
  from sklearn.base import BaseEstimator, TransformerMixin
  from sktime.dists_kernels.base import BasePairwiseTransformerPanel
label_desc: Use image node_id 23 for calling a global function with example usage: hadamard_low_rank(K, R) and returns: LRdec