feedforward network model (default=2048).
  * **dropout** (*float*) -- the dropout value (default=0.1).

  * **activation** (*Union**[**str**,
    **Callable**[**[**Tensor**]**, **Tensor**]**]*) -- the
    activation function of the intermediate layer, can be a string
    ("relu" or "gelu") or a unary callable. Default: relu

  * **layer_norm_eps** (*float*) -- the eps value in layer
    normalization components (default=1e-5).

  * **batch_first** (*bool*) -- If "True", then the input and
    output tensors are provided as (batch, seq, feature). Default:
    "False" (seq, batch, feature).

  * **norm_first** (*bool*) -- if "True", layer norm is done prior
    to self attention, multihead attention and feedforward
    operations, respectively. Otherwise it's done after. Default:
    "False" (after).

Examples::
      >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
      >>> memory = torch.rand(10, 32, 512)