class_docstr: Transformer block described in ViT.
  
  Paper: https://arxiv.org/abs/2010.11929
  Based on: https://github.com/lucidrains/vit-pytorch
  
  Args:
      dim: input dimension.
      depth: depth for transformer block.
      heads: number of heads in multi-head attention layer.
      dim_head: head size.
      mlp_dim: dimension of the FeedForward layer.
      dropout: dropout ratio, defaults to 0.
class_docstr_tok: ["Transformer","block","described","in","ViT",".","Paper",":","https",":","\/\/arxiv.org\/abs\/2010.11929","Based","on",":","https",":","\/\/github.com\/lucidrains\/vit-pytorch","Args",":","dim",":","input","dimension",".","depth",":","depth","for","transformer","block",".","heads",":","number","of","heads","in","multi-head","attention","layer",".","dim_head",":","head","size",".","mlp_dim",":","dimension","of","the","FeedForward","layer",".","dropout",":","dropout","ratio",",","defaults","to","0","."]
code: def forward(self, x: Tensor) -> Tensor:
      for attn, ff in self.layers:
          x = attn(x) + x
          x = ff(x) + x
      return x
  
imports: from typing import Any, Dict, Tuple
  import torch
  from torch import nn
  from kornia.core import Module, Tensor
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.forward(x) and returns: x