k-th layer, of shape (hidden_size)
  * **bias_hh_l[k]** -- the learnable hidden-hidden bias of the
    k-th layer, of shape *(hidden_size)*

Note:
 All the weights and biases are initialized from
 \mathcal{U}(-\sqrt{k}, \sqrt{k}) where k =
 \frac{1}{\text{hidden\_size}}

Note:
 For bidirectional RNNs, forward and backward are directions 0 and
 1 respectively. Example of splitting the output layers when
 "batch_first=False": "output.view(seq_len, batch, num_directions,
 hidden_size)".

Note:
 "batch_first" argument is ignored for unbatched inputs.

Warning:
 There are known non-determinism issues for RNN functions on some
 versions of cuDNN and CUDA. You can enforce deterministic
 behavior by setting the following environment variables:On CUDA
 10.1, set environment variable "CUDA_LAUNCH_BLOCKING=1". This may
 affect performance.On CUDA 10.2 or later, set environment
 variable (note the leading colon symbol)
