Error while replicating Keras-LSTM example

[ishcha]

I am trying to replicate Keras-LSTM DeepExplainer example. I am getting the following error when trying to compute the shap values:
This warning: keras is no longer supported, please use tf.keras instead.
Your TensorFlow version is newer than 2.4.0 and so graph support has been removed in eager mode and some static graphs may not be supported. See PR #1483 for discussion.
And this error:
`
TypeError Traceback (most recent call last)
in
1 import shap
2 explainer = shap.DeepExplainer(model, x_train[:100])
----> 3 shap_values = explainer.shap_values(x_test[:10])

~/miniconda3/envs/mtq/lib/python3.8/site-packages/shap/explainers/_deep/init.py in shap_values(self, X, ranked_outputs, output_rank_order, check_additivity)
122 were chosen as "top".
123 """
--> 124 return self.explainer.shap_values(X, ranked_outputs, output_rank_order, check_additivity=check_additivity)

~/miniconda3/envs/mtq/lib/python3.8/site-packages/shap/explainers/_deep/deep_tf.py in shap_values(self, X, ranked_outputs, output_rank_order, check_additivity)
306 # run attribution computation graph
307 feature_ind = model_output_ranks[j,i]
--> 308 sample_phis = self.run(self.phi_symbolic(feature_ind), self.model_inputs, joint_input)
309
310 # assign the attributions to the right part of the output arrays

~/miniconda3/envs/mtq/lib/python3.8/site-packages/shap/explainers/_deep/deep_tf.py in run(self, out, model_inputs, X)
363
364 return final_out
--> 365 return self.execute_with_overridden_gradients(anon)
366
367 def custom_grad(self, op, *grads):

~/miniconda3/envs/mtq/lib/python3.8/site-packages/shap/explainers/_deep/deep_tf.py in execute_with_overridden_gradients(self, f)
399 # define the computation graph for the attribution values using a custom gradient-like computation
400 try:
--> 401 out = f()
402 finally:
403 # reinstate the backpropagatable check

~/miniconda3/envs/mtq/lib/python3.8/site-packages/shap/explainers/_deep/deep_tf.py in anon()
356 shape = list(self.model_inputs[i].shape)
357 shape[0] = -1
--> 358 data = X[i].reshape(shape)
359 v = tf.constant(data, dtype=self.model_inputs[i].dtype)
360 inputs.append(v)

TypeError: 'NoneType' object cannot be interpreted as an integer
`

I have checked out the PR#1483, but couldn't find a relevant fix there. Please suggest on what tensorflow, keras, and shap versions are needed to successfully replicate the example.

[CloseChoice]

Thanks for the report. Could you please provide a self-contained minimal reproducible example?

[CloseChoice]

Potentially related to #3419