An equivalent for python tflearn library in matlab?

I am currently working on a reinforcement learning problem. I have the following code in python, however I am not sure how is it possible to translate it into matlab!

Python Code:

def create_network(self):
  inputs = tflearn.input_data(shape=[None, self.state_dimension])
  net = tflearn.fully_connected(inputs, 400)
  net = tflearn.layers.normalization.batch_normalization(net)
  net = tflearn.activations.relu(net)
  net = tflearn.fully_connected(net, 300)
  net = tflearn.layers.normalization.batch_normalization(net)
  net = tflearn.activations.relu(net)

    w_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003)
    out = tflearn.fully_connected(
        net, self.a_dim, activation='tanh', weights_init=w_init)

    scaled_out = tf.multiply(out, self.action_bound)
    return inputs, out, scaled_out

I know that Matlab has a toolbox for neural networks, however I still can not figure out how to translate such a code into a Matlab script. I must say I am pretty new to Python so my question might look trivial. I tried to install PyCharm to debug the code and understand what each attribute function is doing, but it still looks like a black box to me. If anyone has a suggestion or a solution, please share it with me. Also it would be nice if someone can explain what kind of calculations or assumptions these attributes are performing?

Thank you so much.