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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def approximate_gradient(decision_function, sample, num_evals, delta, constraint, shape, clip_min, clip_max):
 """ Gradient direction estimation """

  # Generate random vectors.
  noise_shape = [num_evals] + list(shape)
  if constraint == 'l2':
    rv = np.random.randn(*noise_shape)
  elif constraint == 'linf':
    rv = np.random.uniform(low=-1, high=1, size=noise_shape)

  axis = tuple(range(1, 1 + len(shape)))
  rv = rv / np.sqrt(np.sum(rv ** 2, axis=axis, keepdims=True))
  perturbed = sample + delta * rv
  perturbed = clip_image(perturbed, clip_min, clip_max)
  rv = (perturbed - sample) / delta

  # query the model.
  decisions = decision_function(perturbed)
  decision_shape = [len(decisions)] + [1] * len(shape)
  fval = 2 * decisions.astype(np_dtype).reshape(decision_shape) - 1.0

  # Baseline subtraction (when fval differs)
  if np.mean(fval) == 1.0:  # label changes.
    gradf = np.mean(rv, axis=0)
  elif np.mean(fval) == -1.0:  # label not change.
    gradf = - np.mean(rv, axis=0)
  else:
    fval = fval - np.mean(fval)
    gradf = np.mean(fval * rv, axis=0)

  # Get the gradient direction.
  gradf = gradf / np.linalg.norm(gradf)

  return gradf