RNNs redesign

[CarloLucibello]

A complete rework of our recurrent layers, making them more similar to their pytorch counterpart.
This is in line with the proposal in #1365 and should allow to hook into the cuDNN machinery (future PR).
Hopefully, this ends the infinite source of troubles that the recurrent layers have been.

• Recur is no more. Mutating its internal state was a source of problems for AD (explicit differentiation for RNN gives wrong results #2185)
• Now RNNCell is exported and takes care of the minimal recursion step, i.e. a single time:
  • has forward cell(x , h)
  • x can be of size in or in x batch_size
  • h can be of size out or out x batch_size
  • returns hnew of size out or out x batch_size
• RNN instead takes in a (batched) sequence and a (batched) hidden state and returns the hidden state for the whole sequence:
  • has forward rnn(x, h)
  • x can be of size in x len or in x len x batch_size
  • h can be of size out or out x batch_size
  • returns hnew of size out x len or out x len x batch_size
• LSTM and GRU are similarly changed.

Close #2185, close #2341, close #2258, close #1547, close #807, close #1329

Related to #1678

PR Checklist

• cpu tests
• gpu tests
• if hidden state not given as input, assumed to be zero
• port LSTM and GRU
• Entry in NEWS.md
• Remove reset!
• Docstrings
• Benchmarks
• use cuDNN (future PR)
• implement the num_layers argument for stacked RNNs (future PR)
• revisit whole documentation (future PR)
• add dropout (future PR)