human
Solve the following python programming problem given the following code:
# Define style loss function

# Each layer has a style weight that defines how much value to give to each layer's style
STYLE_LAYERS = [
    ('conv1_1', 0.5),
    ('conv2_1', 1.0),
    ('conv3_1', 1.5),
    ('conv4_1', 3.0),
    ('conv5_1', 4.0),
]

def style_loss_func(sess, model):
    def _gram_matrix(F, N, M):
        Ft = tf.reshape(F, (M, N))
        return tf.matmul(tf.transpose(Ft), Ft)

    def _style_loss(a, x):
        # N is the number of filters (at layer l).
        N = a.shape[3]
        # M is the height times the width of the feature map (at layer l).
        M = a.shape[1] * a.shape[2]
        # A is the style representation of the original image (at layer l).
        A = _gram_matrix(a, N, M)
        # G is the style representation of the generated image (at layer l).
        G = _gram_matrix(x, N, M)
        return tf.reduce_sum(tf.pow(G - A, 2))

    E = [_style_loss(sess.run(model[layer_name]), model[layer_name]) for layer_name, _ in STYLE_LAYERS]
    W = [w for _, w in STYLE_LAYERS]
    loss = sum([W[l] * E[l] for l in range(len(STYLE_LAYERS))])
    return loss
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Defining the style loss
We are considering layers 0, 5, 10, 19, 28 for style losses