code: def jl_fit_transform(X, objective_dim, method="basic"):
      """Fit and transform the input data by Johnson–Lindenstrauss process.
      See :cite:`johnson1984extensions` for details.
  
      Parameters
      ----------
      X : numpy array of shape (n_samples, n_features)
          The input samples.
  
      objective_dim : int
          The expected output dimension.
  
      method : string, optional (default = 'basic')
          The JL projection method:
  
          - "basic": each component of the transformation matrix is taken at
            random in N(0,1).
          - "discrete", each component of the transformation matrix is taken at
            random in {-1,1}.
          - "circulant": the first row of the transformation matrix is taken at
            random in N(0,1), and each row is obtained from the previous one
            by a one-left shift.
          - "toeplitz": the first row and column of the transformation matrix
            is taken at random in N(0,1), and each diagonal has a constant value
            taken from these first vector.
  
      Returns
      -------
      X_transformed : numpy array of shape (n_samples, objective_dim)
          The dataset after the JL projection.
  
      jl_transformer : object
          Transformer instance.
      """
      if method.lower() == "basic":
          jl_transformer = (
              1 / math.sqrt(objective_dim)
          ) * np.random.normal(0, 1, size=(objective_dim, len(X[0])))
      elif method.lower() == "discrete":
          jl_transformer = (
              1 / math.sqrt(objective_dim)
          ) * np.random.choice([-1, 1], size=(objective_dim, len(X[0])))
      elif method.lower() == "circulant":
          from scipy.linalg import circulant
  
          first_row = np.random.normal(0, 1, size=(1, len(X[0])))
          jl_transformer = (
              (1 / math.sqrt(objective_dim)) * circulant(first_row)
          )[:objective_dim]
      elif method.lower() == "toeplitz":
          from scipy.linalg import toeplitz
  
          first_row = np.random.normal(0, 1, size=(1, len(X[0])))
          first_column = np.random.normal(0, 1, size=(1, objective_dim))
          jl_transformer = (1 / math.sqrt(objective_dim)) * toeplitz(
              first_column, first_row
          )
      else:
          NotImplementedError("Wrong transformation type")
  
      jl_transformer = jl_transformer.T
  
      return np.dot(X, jl_transformer), jl_transformer
  
imports: import numpy
  from sklearn.utils import check_array
  import math
label_desc: Use image node_id 1 for calling a global function with example usage: jl_fit_transform(X, objective_dim, method) and returns: np,  jl_transformer