def one_step_attention(self, a, s_prev,t0):
        repeator = RepeatVector(Tx)
        concatenator = Concatenate(axis=-1)
        densor1 = Dense(10, activation = "tanh")
        densor2 = Dense(1, activation = "relu")
        activator = Activation(self.softmax, name='attention_weights') # We are using a custom softmax(axis = 1) loaded in this notebook
        dotor = Dot(axes = 1)
        # Use repeator to repeat s_prev to be of shape (m, Tx, n_s) so that you can concatenate it with all hidden states "a".
        s_prev = repeator(s_prev)
        # Use concatenator to concatenate a and s_prev on the last axis
        concat = concatenator([s_prev,a])
        # Use densor1 to propagate concat through a small fully-connected neural network to compute the "intermediate energies" variable e.
        e = densor1(concat)
        # Use densor2 to propagate e through a small fully-connected neural network to compute the "energies" variable energies.
        energies = densor2(e)
        # Use "activator" on "energies" to compute the attention weights "alphas"
        energies = Subtract(name='data-time')([energies,t0])
        alphas = activator(energies)
        # Use dotor together with "alphas" and "a" to compute the context vector to be given to the next layer
        context = dotor([alphas,a])
        return context