In Gradient Tape Tensorflow gradients are coming as 0.s - tensorflow2.0

Question

Here is part of a model I am working. Being new in calculation gradients in tf, I got confused when I found all the gradient values are coming as 0. Here is the code:
class A:
def __init__(self, inputA_dim, inputB_dim):
self.inputA_dim = (35, 35, 1)
self.inputB_dim = 2
self.model = self.nn_model()
self.opt = tf.keras.optimizers.Adam()
# print(self.model.summary())
def nn_model(self):
inputA = Input(self.inputA_dim)
conv1 = Conv2D(10, 3, padding="same", activation="relu")(inputA)
pool1 = MaxPool2D(padding='same')(conv1)
conv2 = Conv2D(10, 3, padding="same", activation="relu")(pool1)
pool2 = MaxPool2D(padding='same')(conv2)
conv3 = Conv2D(10, 3, padding="same", activation="relu")(pool2)
pool3 = MaxPool2D(padding='same')(conv3)
flatten = Flatten()(pool3)
s2 = Dense(32, activation="relu")(flatten)
s3 = Dense(32, activation="relu")(s2)
s4 = Dense(2, activation="relu")(s3)
inputB = Input((self.inputB_dim,))
a1 = Dense(2, activation="relu")(inputB)
c1 = concatenate([s2, a1], axis=-1)
c2 = Dense(4, activation="relu")(c1)
outputs = Dense(1, activation="linear")(c2)
return tf.keras.Model([inputA, inputB], outputs)
def predict(self, inputs):
return self.model.predict(inputs)
def gradients(self, inputA, inputB):
inputB = tf.convert_to_tensor(inputB)
with tf.GradientTape() as tape:
tape.watch(inputB)
values = self.model([inputA, inputB])
values = tf.squeeze(values)
g = tape.gradient(values, inputB)
print(g)
return g
Later I found there is another method called jacobian which I also used here, still giving 0.s asgrad values. Can anyone tell me what to do. Thanks.