class_docstr: An attention block that allows spatial positions to attend to each other.
code: def forward(self, x, mask=None, qk_bias=0):
      b, c, *spatial = x.shape
      if mask is not None:
          if len(mask.shape) == 2:
              mask = mask.unsqueeze(0).repeat(x.shape[0], 1, 1)
          if mask.shape[1] != x.shape[-1]:
              mask = mask[:, : x.shape[-1], : x.shape[-1]]
  
      x = x.reshape(b, c, -1)
      x = self.norm(x)
      if self.do_activation:
          x = F.silu(x, inplace=True)
      qkv = self.qkv(x)
      h = self.attention(qkv, mask=mask, qk_bias=qk_bias)
      h = self.proj_out(h)
      xp = self.x_proj(x)
      return (xp + h).reshape(b, xp.shape[1], *spatial)
  
imports: import math
  import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the AttentionBlock obj's underlying member method code with example usage: obj.forward(x, mask, qk_bias) and returns: unknown