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 _recombine_heads(self, x: Tensor) -> Tensor:
      b, n_heads, n_tokens, c_per_head = x.shape
      x = x.transpose(1, 2)
      return x.reshape(b, n_tokens, n_heads * c_per_head)
  
imports: from __future__ import annotations
  import math
  from torch import nn
  from kornia.contrib.models.sam.architecture.common import MLPBlock
  from kornia.core import Module, Tensor
  from kornia.core.check import KORNIA_CHECK
label_desc: Use image node_id 3 for calling the Attention obj's underlying member method code with example usage: obj._recombine_heads(x) and returns: x