docstr: Euclidean distance: :math:`k(x, y) = ||x-y||`. A forward pass takes a batch of instances `x` of
  size `Nx x f1 x f2 x ... ` and `y` of size `Ny x f1 x f2 x ...` and returns the kernel matrix `Nx x Ny`.
docstr_tok: ["Euclidean","distance",":",":","math",":","`","k","(","x",",","y",")","=","||x-y||","`",".","A","forward","pass","takes","a","batch","of","instances","`","x","`","of","size","`","Nx","x","f1","x","f2","x","...","`","and","`","y","`","of","size","`","Ny","x","f1","x","f2","x","...","`","and","returns","the","kernel","matrix","`","Nx","x","Ny","`","."]
code: def __init__(self) -> None:
      """
      Euclidean distance: :math:`k(x, y) = ||x-y||`. A forward pass takes a batch of instances `x` of
      size `Nx x f1 x f2 x ... ` and `y` of size `Ny x f1 x f2 x ...` and returns the kernel matrix `Nx x Ny`.
      """
      pass
  
file_path: alibi/alibi/utils/kernel.py
imports: import numpy
  from typing import Union, Optional
  from alibi.utils.distance import squared_pairwise_distance
label_desc: Use image node_id 1 to create a new EuclideanDistance object  with example: obj = EuclideanDistance()