tf.GradientTape() returns None value for my neural network function

So I created my own neural network and I want to do an automatic differentiation for it with respect to the input variable. My code for the neural network goes like this

n_input = 1     
n_hidden_1 = 50 
n_hidden_2 = 50 
n_output = 1 

weights = {
'h1': tf.Variable(tf.random.normal([n_input, n_hidden_1],0,0.5)),
'h2': tf.Variable(tf.random.normal([n_hidden_1, n_hidden_2],0,0.5)),
'out': tf.Variable(tf.random.normal([n_hidden_2, n_output],0,0.5))
}

biases = {
'b1': tf.Variable(tf.random.normal([n_hidden_1],0,0.5)),
'b2': tf.Variable(tf.random.normal([n_hidden_2],0,0.5)),
'out': tf.Variable(tf.random.normal([n_output],0,0.5))
}

def multilayer_perceptron(x):
    x = np.array([[[x]]],  dtype='float32')
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.tanh(layer_1)
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.tanh(layer_2)
    output = tf.matmul(layer_2, weights['out']) + biases['out']
    return output

And with tf.GradientTape(), I tried to differentiate the neural network with this

x = tf.Variable(1.0)
with tf.GradientTape() as tape:
    y = multilayer_perceptron(x)
dNN1 = tape.gradient(y,x)
print(dNN1)

Which results None. What did I do wrong here?

For a good running of some tensorflow operations, it's preferable that all elements of operations are of type tf.tensor, you have to reshape using

def multilayer_perceptron(x):
 x =  tf.reshape(x , (1,1,1))