class_docstr: This is much like `.vision_transformer.Block` but:
      - Called TransformerLayer here to allow for "block" as defined in the paper ("non-overlapping image blocks")
      - Uses modified Attention layer that handles the "block" dimension
class_docstr_tok: ["This","is","much","like","`",".vision_transformer.Block","`","but",":","-","Called","TransformerLayer","here","to","allow","for","``","block","''","as","defined","in","the","paper","(","``","non-overlapping","image","blocks","''",")","-","Uses","modified","Attention","layer","that","handles","the","``","block","''","dimension"]
code: def forward(self, x):
      y = self.norm1(x)
      x = x + self.drop_path(self.attn(y))
      x = x + self.drop_path(self.mlp(self.norm2(x)))
      return x
  
imports: import collections.abc
  import logging
  import math
  from functools import partial
  import torch
  import torch.nn.functional
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, create_classifier, trunc_normal_, _assert
  from timm.layers import create_conv2d, create_pool2d, to_ntuple, use_fused_attn, LayerNorm
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import checkpoint_seq, named_apply
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
label_desc: Use image node_id 2 for calling the TransformerLayer obj's underlying member method code with example usage: obj.forward(x) and returns: x