by dropout \delta^{(l-1)}_t where each \delta^{(l-1)}_t is a
   Bernoulli random variable which is 0 with probability "dropout".
Parameters:
      * input_size -- The number of expected features in the input
        x
  * **hidden_size** -- The number of features in the hidden state
    *h*

  * **num_layers** -- Number of recurrent layers. E.g., setting
    "num_layers=2" would mean stacking two GRUs together to form a
    *stacked GRU*, with the second GRU taking in outputs of the
    first GRU and computing the final results. Default: 1

  * **bias** -- If "False", then the layer does not use bias
    weights *b_ih* and *b_hh*. Default: "True"

  * **batch_first** -- If "True", then the input and output
    tensors are provided as *(batch, seq, feature)* instead of
    *(seq, batch, feature)*. Note that this does not apply to
    hidden or cell states. See the Inputs/Outputs sections below
    for details.  Default: "False"
