Computing the gradients of new state (of the RNN) with respect to model parameters, (including CNN for inputs), in tensorflow

I have the following model in tensorflow:

inputs = tf.layers.conv1d(inputs, filters=100, kernel_size=1, padding="same")
inputs = tf.layers.batch_normalization(inputs, training=phase_train, name="bn")
inputs = tf.nn.relu(inputs)

with tf.variable_scope('lstm_model'):

    cell = tf.nn.rnn_cell.GRUCell(cell_size, kernel_initializer=kernel_initializer)
    # output1: shape=[1, time_steps, 32]
    output, new_state = tf.nn.dynamic_rnn(cell, inputs, dtype=m_dtype)

with tf.variable_scope("output"):
    output = tf.reshape(output, shape=[-1, cell_size])
    output = tf.layers.dense(output, units=num_classes,
                             kernel_initializer=kernel_initializer)

    output = tf.cond(phase_train,
                     lambda: tf.reshape(output, shape=[34, -1, num_classes]),
                     lambda: tf.reshape(output, shape=[14, -1, num_classes]))

    return output, new_state, model_summary

Now when I attempt to find the gradients of the new_state with respect to the model parmaeters, I got an error:

grads_new_state_wrt_vars = tf.gradients(new_state, tf.trainable_variables())

and the error:

TypeError: Fetch argument None has invalid type <class 'NoneType'>

Please note that when I printed out the gradients tensors, I got the following:

for g in grads_new_state_wrt_vars:
    print('**', g)

** None
** None
** None
** None
** Tensor("gradients/model/lstm_model/rnn/while/gru_cell/MatMul/Enter_grad/b_acc_3:0", shape=(220, 240), dtype=float64)
** Tensor("gradients/model/lstm_model/rnn/while/gru_cell/BiasAdd/Enter_grad/b_acc_3:0", shape=(240,), dtype=float64)
** Tensor("gradients/model/lstm_model/rnn/while/gru_cell/MatMul_1/Enter_grad/b_acc_3:0", shape=(220, 120), dtype=float64)
** Tensor("gradients/model/lstm_model/rnn/while/gru_cell/BiasAdd_1/Enter_grad/b_acc_3:0", shape=(120,), dtype=float64)
** None
** None

Finally, the weights in the network are printed below:

for v in tf.trainable_variables():
    print(v.name)

model/conv1d/kernel:0
model/conv1d/bias:0
model/bn/gamma:0
model/bn/beta:0
model/lstm_model/rnn/gru_cell/gates/kernel:0
model/lstm_model/rnn/gru_cell/gates/bias:0
model/lstm_model/rnn/gru_cell/candidate/kernel:0
model/lstm_model/rnn/gru_cell/candidate/bias:0
model/output/dense/kernel:0
model/output/dense/bias:0

Therefore, how come that the gradients can't be computed wrt to the weights of the first conv, and batch norm, layers in the network?

Please note that I don't have the same problem when replacing new_state by output.

Any help is much appreciated!!

from Computing the gradients of new state (of the RNN) with respect to model parameters, (including CNN for inputs), in tensorflow