torch.cdist
torch.cdist(x1, x2, p=2.0, compute_mode='use_mm_for_euclid_dist_if_necessary')
Computes batched the p-norm distance between each pair of the two
   collections of row vectors.
Parameters:
      * x1 (Tensor) -- input tensor of shape B \times P \times
        M.
  * **x2** (*Tensor*) -- input tensor of shape B \times R \times
    M.

  * **p** (*float*) -- p value for the p-norm distance to
    calculate between each vector pair \in [0, \infty].

  * **compute_mode** (*str*) --
    'use_mm_for_euclid_dist_if_necessary' - will use matrix
    multiplication approach to calculate euclidean distance (p =
    2) if P > 25 or R > 25 'use_mm_for_euclid_dist' - will always
    use matrix multiplication approach to calculate euclidean
    distance (p = 2) 'donot_use_mm_for_euclid_dist' - will never
    use matrix multiplication approach to calculate euclidean
    distance (p = 2) Default: use_mm_for_euclid_dist_if_necessary.
