class_docstr: An attention layer that allows for downscaling the size of the embedding
  after projection to queries, keys, and values.
class_docstr_tok: ["An","attention","layer","that","allows","for","downscaling","the","size","of","the","embedding","after","projection","to","queries",",","keys",",","and","values","."]
code: def forward(self, q, k, v):
      q = self.q_proj(q)
      k = self.k_proj(k)
      v = self.v_proj(v)
      q = self._separate_heads(q, self.num_heads)
      k = self._separate_heads(k, self.num_heads)
      v = self._separate_heads(v, self.num_heads)
      _, _, _, c_per_head = q.shape
      attn = q @ k.transpose([0, 1, 3, 2])
      attn = attn / math.sqrt(c_per_head)
      attn = F.softmax(attn, axis=-1)
      out = attn @ v
      out = self._recombine_heads(out)
      out = self.out_proj(out)
      return out
  
file_path: PaddleSeg/contrib/SegmentAnything/segment_anything/modeling/transformer.py
imports: import paddle
  from paddle import Tensor, nn
  import paddle.nn.functional
  import math
  from typing import Tuple, Type
  from common import MLPBlock
label_desc: Use image node_id 4 for calling the Attention obj's underlying member method code with example usage: obj.forward(q, k, v) and returns: out