class_docstr: A multi-layer perceptron block, based on: "Dosovitskiy et al.,
  An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
docstr: Args:
      hidden_size: dimension of hidden layer.
      mlp_dim: dimension of feedforward layer. If 0, `hidden_size` will be used.
      dropout_rate: fraction of the input units to drop.
      act: activation type and arguments. Defaults to GELU. Also supports "GEGLU" and others.
      dropout_mode: dropout mode, can be "vit" or "swin".
          "vit" mode uses two dropout instances as implemented in
          https://github.com/google-research/vision_transformer/blob/main/vit_jax/models.py#L87
          "swin" corresponds to one instance as implemented in
          https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_mlp.py#L23
docstr_tok: ["Args",":","hidden_size",":","dimension","of","hidden","layer",".","mlp_dim",":","dimension","of","feedforward","layer",".","If","0",",","`","hidden_size","`","will","be","used",".","dropout_rate",":","fraction","of","the","input","units","to","drop",".","act",":","activation","type","and","arguments",".","Defaults","to","GELU",".","Also","supports","``","GEGLU","''","and","others",".","dropout_mode",":","dropout","mode",",","can","be","``","vit","''","or","``","swin","''",".","``","vit","''","mode","uses","two","dropout","instances","as","implemented","in","https",":","\/\/github.com\/google-research\/vision_transformer\/blob\/main\/vit_jax\/models.py","#","L87","``","swin","''","corresponds","to","one","instance","as","implemented","in","https",":","\/\/github.com\/microsoft\/Swin-Transformer\/blob\/main\/models\/swin_mlp.py","#","L23"]
code: def __init__(
      self,
      hidden_size: int,
      mlp_dim: int,
      dropout_rate: float = 0.0,
      act: tuple | str = "GELU",
      dropout_mode="vit",
  ) -> None:
      """
      Args:
          hidden_size: dimension of hidden layer.
          mlp_dim: dimension of feedforward layer. If 0, `hidden_size` will be used.
          dropout_rate: fraction of the input units to drop.
          act: activation type and arguments. Defaults to GELU. Also supports "GEGLU" and others.
          dropout_mode: dropout mode, can be "vit" or "swin".
              "vit" mode uses two dropout instances as implemented in
              https://github.com/google-research/vision_transformer/blob/main/vit_jax/models.py#L87
              "swin" corresponds to one instance as implemented in
              https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_mlp.py#L23
  
  
      """
  
      super().__init__()
  
      if not (0 <= dropout_rate <= 1):
          raise ValueError("dropout_rate should be between 0 and 1.")
      mlp_dim = mlp_dim or hidden_size
      self.linear1 = (
          nn.Linear(hidden_size, mlp_dim)
          if act != "GEGLU"
          else nn.Linear(hidden_size, mlp_dim * 2)
      )
      self.linear2 = nn.Linear(mlp_dim, hidden_size)
      self.fn = get_act_layer(act)
      self.drop1 = nn.Dropout(dropout_rate)
      dropout_opt = look_up_option(dropout_mode, SUPPORTED_DROPOUT_MODE)
      if dropout_opt == "vit":
          self.drop2 = nn.Dropout(dropout_rate)
      elif dropout_opt == "swin":
          self.drop2 = self.drop1
      else:
          raise ValueError(
              f"dropout_mode should be one of {SUPPORTED_DROPOUT_MODE}"
          )
  
file_path: MONAI/monai/networks/blocks/mlp.py
imports: from __future__ import annotations
  import torch.nn
  from monai.networks.layers import get_act_layer
  from monai.utils import look_up_option
label_desc: Use image node_id 1 to create a new MLPBlock object from inherited base classes: nn with example: obj = MLPBlock(hidden_size, mlp_dim, dropout_rate, act, dropout_mode)