Enhance support for 'tf.IndexedSlices' in 'TensorflowVector'.

This MR is designed to close issue #17 (closed), by extending the backend of TensorflowVector to preserve tf.IndexedSlices structures when conducting operations that are mathematically compatible with their sparsity.

As a consequence, in most cases, tf.IndexedSlices gradients will result in tf.IndexedSlices updates, avoiding unrequired memory use and runtime costs for zero-valued rows. One exception is when such a structure is combined with a dense tensor matching their dense shape: then, they are densified, with a warning. The latter is silenced in the one context where this is currently used: as part of NoiseModule.run, i.e. when adding some random noise to the gradients (which includes its non-zero values), typically as part of the DP-SGD algorithm.

One question that remains open is how loss-regularization term should factor for the fact that some weights are unused as part of a given SGD step. At the moment, Regularizer algorithms compute gradient corrections based on the full-rank model weights, so that tf.IndexedSlices gradients will be densified (with a warning). It might be interesting to figure out whether this makes sense, or if some weights should be ignored as part of these computations - but this may for the moment be left as a reflection for end-users, that are supposed to be responsible of the validity of the maths at work in their models and federated optimization algorithm choices.

This MR:

• Modifies the backend of TensorflowVector, notably using the newly-introduced add_indexed_slices_support function exposed under declearn.model.tensorflow.utils.
• Modifies the existing unit tests to have random-valued tensorflow gradients include some (non-sparse) tf.IndexedSlices structures together with the classical tf.Tensor ones.

Closes #17 (closed)