ValueError: No gradients provided for any variable in GanModel with tap.gradiant - generative-adversarial-network

Question

I am building GAN Model for image captioning, that generator produce a word. then we call generator to the maximum length of the sentence. and then we give the generated sentence to the discriminator and update the network using these and loss function, but I got error.
optimizer = tf.keras.optimizers.Adam(1e-5)
for name in train:
print(name)
feature=train_features[name].reshape(1,train_features[name].shape[0],
train_features[name].shape[1],
train_features[name].shape[2])
for desc in train_descriptions[name]:
with tf.GradientTape(persistent=True) as tape:
in_text = 'startseq'
for i in range(1, max_length):
sequence = tokenizer.texts_to_sequences([in_text])[0]
sequence = pad_sequences([sequence], maxlen=max_length)
yhat=G([feature,sequence])[0]
## find index of word
word = tf.math.argmax(yhat)
## convert to word
word = word_for_idV2(word, tokenizer)
in_text += ' ' + word
if word == 'endseq':
break
fake_data = tokenizer.texts_to_sequences([in_text])[0]
fake_data = pad_sequences([fake_data], maxlen=max_length)
d_fake_data=D([feature,fake_data])
print("==================================")
print(d_fake_data)
real_data = tf.convert_to_tensor(tokenizer.texts_to_sequences([desc]),dtype=tf.float32)
print(real_data)
g_loss_value = g_loss2(loss_d=d_fake_data,real_desc=real_data)
print("g_loss_value")
print(g_loss_value)
# Now that we have computed the losses, we can compute the gradients
# (using the tape) and optimize the networks
print(G.trainable_variables)
g_gradients = tape.gradient(g_loss_value, G.trainable_variables)
print("g_gradients")
print(g_gradients)
optimizer.apply_gradients(zip(g_gradients, G.trainable_variables))
Error code is below.
ValueError: No gradients provided for any variable: ['conv2d/kernel:0', 'conv2d/bias:0', 'conv2d_1/kernel:0', 'conv2d_1/bias:0', 'conv2d_2/kernel:0', 'conv2d_2/bias:0', 'conv2d_3/kernel:0', 'conv2d_3/bias:0', 'embedding/embeddings:0', 'lstm/kernel:0', 'lstm/recurrent_kernel:0', 'lstm/bias:0', 'lstm_1/kernel:0', 'lstm_1/recurrent_kernel:0', 'lstm_1/bias:0', 'dense/kernel:0', 'dense/bias:0', 'dense_1/kernel:0', 'dense_1/bias:0', 'lstm_2/kernel:0', 'lstm_2/recurrent_kernel:0', 'lstm_2/bias:0', 'decoder_output/kernel:0', 'decoder_output/bias:0'].