TransformerEncoder
class torch.nn.TransformerEncoder(encoder_layer, num_layers, norm=None, enable_nested_tensor=True, mask_check=True)
TransformerEncoder is a stack of N encoder layers. Users can build
   the BERT(https://arxiv.org/abs/1810.04805) model with corresponding
   parameters.
Parameters:
      * encoder_layer -- an instance of the
        TransformerEncoderLayer() class (required).
  * **num_layers** -- the number of sub-encoder-layers in the
    encoder (required).

  * **norm** -- the layer normalization component (optional).

  * **enable_nested_tensor** -- if True, input will automatically
    convert to nested tensor (and convert back on output). This
    will improve the overall performance of TransformerEncoder
    when padding rate is high. Default: "True" (enabled).

Examples::
      >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)