class_docstr: Computes attention between every entry in matrix_1 with every entry in matrix_2 using a dot
  product. Scales the result by the size of the embeddings.
  
  Registered as a `MatrixAttention` with name "scaled_dot_product".
class_docstr_tok: ["Computes","attention","between","every","entry","in","matrix_1","with","every","entry","in","matrix_2","using","a","dot","product",".","Scales","the","result","by","the","size","of","the","embeddings",".","Registered","as","a","`","MatrixAttention","`","with","name","``","scaled_dot_product","''","."]
code: def forward(
      self, matrix_1: torch.Tensor, matrix_2: torch.Tensor
  ) -> torch.Tensor:
      return super().forward(matrix_1, matrix_2) / math.sqrt(
          matrix_1.size(-1)
      )
  
imports: import math
  import torch
  from allennlp.modules.matrix_attention.dot_product_matrix_attention import DotProductMatrixAttention
  from allennlp.modules.matrix_attention.matrix_attention import MatrixAttention
label_desc: Use image node_id 1 for calling the ScaledDotProductMatrixAttention obj's underlying member method code with example usage: obj.forward(matrix_1, matrix_2) and returns: unknown