How to use the gradient.check_gradient function in gradient

sparsity_param, beta, patches)
print cost, grad
##======================================================================
## STEP 3: Gradient Checking
#
# Hint: If you are debugging your code, performing gradient checking on smaller models
# and smaller training sets (e.g., using only 10 training examples and 1-2 hidden
# units) may speed things up.

# First, lets make sure your numerical gradient computation is correct for a
# simple function.  After you have implemented computeNumericalGradient.m,
# run the following:


if debug:
    gradient.check_gradient()

    # Now we can use it to check your cost function and derivative calculations
    # for the sparse autoencoder.
    # J is the cost function

    J = lambda x: sparse_autoencoder.sparse_autoencoder_cost(x, visible_size, hidden_size,
                                                             lambda_, sparsity_param,
                                                             beta, patches)
    num_grad = gradient.compute_gradient(J, theta)

    # Use this to visually compare the gradients side by side
    print num_grad, grad

    # Compare numerically computed gradients with the ones obtained from backpropagation
    diff = np.linalg.norm(num_grad - grad) / np.linalg.norm(num_grad + grad)
    print diff