CosineSimilarity
class torch.nn.CosineSimilarity(dim=1, eps=1e-08)
Returns cosine similarity between x_1 and x_2, computed along
   dim.
  \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert
  _2 \cdot \Vert x_2 \Vert _2, \epsilon)}.

Parameters:
      * dim (int, optional) -- Dimension where cosine
        similarity is computed. Default: 1
  * **eps** (*float**, **optional*) -- Small value to avoid
    division by zero. Default: 1e-8

Shape:
      * Input1: (\ast_1, D, \ast_2) where D is at position dim
  * Input2: (\ast_1, D, \ast_2), same number of dimensions as x1,
    matching x1 size at dimension *dim*,
       and broadcastable with x1 at other dimensions.

  * Output: (\ast_1, \ast_2)

Examples::
      >>> input1 = torch.randn(100, 128)
      >>> input2 = torch.randn(100, 128)
      >>> cos = nn.CosineSimilarity(dim=1, eps=1e-6)
      >>> output = cos(input1, input2)