human
Solve the following python programming problem given the following code:
# Get the gradients of the generated image wrt the loss
grads = K.gradients(loss, combination_image)[0]

# Function to fetch the values of the current loss and the current gradients
fetch_loss_and_grads = K.function([combination_image], [loss, grads])

class Evaluator(object):

    def __init__(self):
        self.loss_value = None
        self.grads_values = None

    def loss(self, x):
        assert self.loss_value is None
        x = x.reshape((1, img_height, img_width, 3))
        outs = fetch_loss_and_grads([x])
        loss_value = outs[0]
        grad_values = outs[1].flatten().astype('float64')
        self.loss_value = loss_value
        self.grad_values = grad_values
        return self.loss_value

    def grads(self, x):
        assert self.loss_value is not None
        grad_values = np.copy(self.grad_values)
        self.loss_value = None
        self.grad_values = None
        return grad_values

evaluator = Evaluator()
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Finally, we set up the gradient descent process. In the original Gatys et al. paper, optimization is performed using the L-BFGS algorithm, 
so that is also what we will use here. This is a key difference from the Deep Dream example in the previous section. The L-BFGS algorithms 
comes packaged with SciPy. However, there are two slight limitations with the SciPy implementation:

It requires to be passed the value of the loss function and the value of the gradients as two separate functions.
It can only be applied to flat vectors, whereas we have a 3D image array.

It would be very inefficient for us to compute the value of the loss function and the value of gradients independently, since it would lead 
to a lot of redundant computation between the two. We would be almost twice slower than we could be by computing them jointly. To by-pass 
this, we set up a Python class named Evaluator that will compute both loss value and gradients value at once, will return the loss value 
when called the first time, and will cache the gradients for the next call.