After converting a model from tf1 to tf2, I cannot use the tf2 model with tf.GradientTape().

Some useful information:

• The model uses tfa.seq2seq layers, which are most likely at the source of the error (e.g. replacing the content of the function network below with a single tf.compat.v1.layers.Dense works fine)
• tensorflow 2.9.1
• Python 3.9

Here are 2 snippets to reproduce the error:

• First run this snippet to create/save the model

import functools
import tensorflow as tf
import tensorflow_addons as tfa

MODEL_FOLDER = "model"
LSTM_UNITS = 3

def network(input):
    attention_mechanism = tfa.seq2seq.BahdanauAttention(
        1, input, memory_sequence_length=None
    )
    cell = tf.compat.v1.nn.rnn_cell.LSTMCell(
      LSTM_UNITS,
    )
    attention_cell = tfa.seq2seq.AttentionWrapper(
        cell, attention_mechanism, output_attention=False
    )

    embedding_fn = functools.partial(tf.compat.v1.one_hot, depth=10)
    output_layer = tf.compat.v1.layers.Dense(
        10,
    )

    train_helper = tfa.seq2seq.GreedyEmbeddingSampler(embedding_fn)
    decoder = tfa.seq2seq.BasicDecoder(
            cell=attention_cell, sampler=train_helper, output_layer=output_layer
    )
    init_kwargs = {}
    init_kwargs["start_tokens"] = tf.compat.v1.fill([1], 1)
    init_kwargs["end_token"] = 2
    init_kwargs["initial_state"] = attention_cell.get_initial_state(
        batch_size=1, dtype=tf.compat.v1.float32
    )
    outputs, _, output_lengths = tfa.seq2seq.dynamic_decode(
        decoder=decoder,
        output_time_major=False,
        impute_finished=False,
        maximum_iterations=1,
        decoder_init_kwargs=init_kwargs,
    )
    return outputs


def main(_):
    input = tf.compat.v1.placeholder(dtype=tf.compat.v1.float32, shape=[1, 1, LSTM_UNITS])

    fetches = {
        "output": network(input).rnn_output,
    }

    with tf.compat.v1.Session() as sess:
        builder = tf.compat.v1.saved_model.Builder(MODEL_FOLDER)

        sess.run(
            [
                tf.compat.v1.global_variables_initializer(),
                tf.compat.v1.local_variables_initializer(),
                tf.compat.v1.tables_initializer(),
            ]
        )

        sig_def = tf.compat.v1.saved_model.predict_signature_def(
            inputs={"input": input}, outputs=fetches
        )

        builder.add_meta_graph_and_variables(
            sess,
            tags=["serve"],
            signature_def_map={
                tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY: sig_def
            },
        )
        builder.save()

if __name__ == "__main__":

    # Save model
    tf.compat.v1.disable_eager_execution()
    tf.compat.v1.disable_v2_behavior()
    tf.compat.v1.app.run()

• Then run this snippet to load/use the model

import tensorflow as tf

MODEL_FOLDER = "model"
LSTM_UNITS = 3

if __name__ == "__main__":

    # Load model and use it
    input = tf.ones((1, 1, LSTM_UNITS))
    model = tf.saved_model.load(
        MODEL_FOLDER, tags="serve"
    ).signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]

    print(model(input))  # this works
    with tf.GradientTape():
        model(input) # AttributeError: 'NoneType' object has no attribute 'outer_context'

If anyone could help me that would be great, thank you!