class_docstr: gSobol function
  
  :param a: one-dimensional array containing the coefficients of the function.
  :param sd: standard deviation, to generate noisy evaluations of the function.
code: def f(self, X):
      X = reshape(X, self.input_dim)
      n = X.shape[0]
      aux = (abs(4 * X - 2) + np.ones(n).reshape(n, 1) * self.a) / (
          1 + np.ones(n).reshape(n, 1) * self.a
      )
      fval = np.cumprod(aux, axis=1)[:, self.input_dim - 1]
      if self.sd == 0:
          noise = np.zeros(n).reshape(n, 1)
      else:
          noise = np.random.normal(0, self.sd, n).reshape(n, 1)
      return fval.reshape(n, 1) + noise
  
file_path: GPyOpt/GPyOpt/objective_examples/experimentsNd.py
imports: import numpy
  from ..util.general import reshape
label_desc: Use image node_id 2 for calling the gSobol obj's underlying member method code with example usage: obj.f(X) and returns: unknown