code: def jl_transform(X, jl_transformer):
      """Use the fitted transformer to conduct JL projection.
  
      Parameters
      ----------
      X : numpy array of shape (n_samples, n_features)
          The input samples.
  
      jl_transformer : object
          Fitted transformer instance.
  
      Returns
      -------
      X_transformed : numpy array of shape (n_samples, reduced_dimensions)
          Transformed matrix.
      """
      X = check_array(X)
      jl_transformer = check_array(jl_transformer)
  
      # no need for transformation
      if np.array_equal(
          jl_transformer, np.ones([X.shape[1], X.shape[1]])
      ):
          return X
  
      if X.shape[1] != jl_transformer.shape[0]:
          ValueError("X and jl_transformer have different dimensions.")
      return np.dot(X, jl_transformer)
  
imports: import numpy
  from sklearn.utils import check_array
  import math
label_desc: Use image node_id 2 for calling a global function with example usage: jl_transform(X, jl_transformer) and returns: np, X