Fix the Tensorflow/Keras Tests

[chukarsten]

With #1 we skipped the Tensorflow/Keras tests (some of them) to keep things green for most non-tf users going forward. We'd like to fix them, though.

The tests that are failing:

FAILED tests/explainers/test_deep.py::test_tf_keras_mnist_cnn - AttributeError: 'Adam' object has no attribute 'get_updates'
FAILED tests/explainers/test_deep.py::test_tf_keras_linear - AttributeError: 'SGD' object has no attribute 'get_updates'
FAILED tests/explainers/test_gradient.py::test_tf_keras_mnist_cnn - AttributeError: 'Adadelta' object has no attribute 'get_updates'

Stack Trace:

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/hostedtoolcache/Python/3.7.16/x64/lib/python3.7/site-packages/keras/engine/training_v1.py:873: in fit
    use_multiprocessing=use_multiprocessing,
/opt/hostedtoolcache/Python/3.7.16/x64/lib/python3.7/site-packages/keras/engine/training_arrays_v1.py:751: in fit
    steps_name="steps_per_epoch",
/opt/hostedtoolcache/Python/3.7.16/x64/lib/python3.7/site-packages/keras/engine/training_arrays_v1.py:192: in model_iteration
    f = _make_execution_function(model, mode)
/opt/hostedtoolcache/Python/3.7.16/x64/lib/python3.7/site-packages/keras/engine/training_arrays_v1.py:620: in _make_execution_function
    return model._make_execution_function(mode)
/opt/hostedtoolcache/Python/3.7.16/x64/lib/python3.7/site-packages/keras/engine/training_v1.py:2364: in _make_execution_function
    self._make_train_function()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <keras.engine.sequential.Sequential object at 0x7fdfe6e91ed0>

    def _make_train_function(self):
        has_recompiled = self._recompile_weights_loss_and_weighted_metrics()
        self._check_trainable_weights_consistency()
        if isinstance(self.optimizer, list):
            raise ValueError(
                "The `optimizer` in `compile` should be a single optimizer."
            )
        # If we have re-compiled the loss/weighted metric sub-graphs then create
        # train function even if one exists already. This is because
        # `_feed_sample_weights` list has been updated on re-compile.
        if getattr(self, "train_function", None) is None or has_recompiled:
            # Restore the compiled trainable state.
            current_trainable_state = self._get_trainable_state()
            self._set_trainable_state(self._compiled_trainable_state)
    
            inputs = (
                self._feed_inputs
                + self._feed_targets
                + self._feed_sample_weights
            )
            if not isinstance(backend.symbolic_learning_phase(), int):
                inputs += [backend.symbolic_learning_phase()]
    
            with backend.get_graph().as_default():
                with backend.name_scope("training"):
                    # Training updates
>                   updates = self.optimizer.get_updates(
                        params=self._collected_trainable_weights,
                        loss=self.total_loss,
                    )
E                   AttributeError: 'Adam' object has no attribute 'get_updates'

It's unclear what's going on as the error is being thrown in keras Engine code that's calling the get_updates() function on a keras optimizer object. We need to figure out what's going on - fix it - then unskip the three tests above.