class_docstr: Attention module that can take tensor with [B, N, C] or [B, C, H, W] as input. Structure specified by metaformer
  Modified from:
  https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
  Args:
      dim (int): dimension
      head_dim (int): head dimension
class_docstr_tok: ["Attention","module","that","can","take","tensor","with","[","B",",","N",",","C","]","or","[","B",",","C",",","H",",","W","]","as","input",".","Structure","specified","by","metaformer","Modified","from",":","https",":","\/\/github.com\/rwightman\/pytorch-image-models\/blob\/master\/timm\/models\/vision_transformer.py","Args",":","dim","(","int",")",":","dimension","head_dim","(","int",")",":","head","dimension"]
code: def forward(self, x):
      shape = x.shape
      if len(shape) == 4:
          b, c, h, w = shape
          n = h * w
          x = torch.flatten(x, start_dim=2).transpose(
              -2, -1
          )  # (B, N, C)
      qkv = (
          self.qkv(x)
          .reshape(b, n, 3, self.num_heads, self.head_dim)
          .permute(2, 0, 3, 1, 4)
      )
      q, k, v = qkv.unbind(
          0
      )  # make torchscript happy (cannot use tensor as tuple)
  
      # trick here to make q@k.t more stable
      attn = (q * self.scale) @ k.transpose(-2, -1)
      # attn = (q @ k.transpose(-2, -1)) * self.scale
      attn = attn.softmax(dim=-1)
      attn = self.attn_drop(attn)
  
      x = (attn @ v).transpose(1, 2).reshape(b, n, c)
      x = self.proj(x)
      x = self.proj_drop(x)
      if len(shape) == 4:
          x = x.transpose(-2, -1).reshape(b, c, h, w)
  
      return x
  
imports: from torch import nn
  import torch
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.forward(x) and returns: x