torch.nn.functional.cosine_similarity
torch.nn.functional.cosine_similarity(x1, x2, dim=1, eps=1e-8) -> Tensor
Returns cosine similarity between "x1" and "x2", computed along
   dim. "x1" and "x2" must be broadcastable to a common shape. "dim"
   refers to the dimension in this common shape. Dimension "dim" of
   the output is squeezed (see "torch.squeeze()"), resulting in the
   output tensor having 1 fewer dimension.
  \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert
  _2 \cdot \Vert x_2 \Vert _2, \epsilon)}

Supports type promotion.
Parameters:
      * x1 (Tensor) -- First input.
  * **x2** (*Tensor*) -- Second input.

  * **dim** (*int**, **optional*) -- Dimension along which cosine
    similarity is computed. Default: 1

  * **eps** (*float**, **optional*) -- Small value to avoid
    division by zero. Default: 1e-8

Example:
  >>> input1 = torch.randn(100, 128)
