code: def exact_gaussian_kernel(x, y, stddev):
      r"""Computes exact Gaussian kernel value(s) for tensors x and y and stddev.
  
      The Gaussian kernel for vectors u, v is defined as follows:
           K(u, v) = exp(-||u-v||^2 / (2* stddev^2))
      where the norm is the l2-norm. x, y can be either vectors or matrices. If they
      are vectors, they must have the same dimension. If they are matrices, they
      must have the same number of columns. In the latter case, the method returns
      (as a matrix) K(u, v) values for all pairs (u, v) where u is a row from x and
      v is a row from y.
  
      Args:
        x: a tensor of rank 1 or 2. It's shape should be either [dim] or [m, dim].
        y: a tensor of rank 1 or 2. It's shape should be either [dim] or [n, dim].
        stddev: The width of the Gaussian kernel.
  
      Returns:
        A single value (scalar) with shape (1, 1) (if x, y are vectors) or a matrix
          of shape (m, n) with entries K(u, v) (where K is the Gaussian kernel) for
          all (u,v) pairs where u, v are rows from x and y respectively.
  
      Raises:
        ValueError: if the shapes of x, y are not compatible.
      """
      x_aligned, y_aligned = _align_matrices(x, y)
      diff_squared_l2_norm = math_ops.reduce_sum(
          math_ops.squared_difference(x_aligned, y_aligned), 2
      )
      return math_ops.exp(-diff_squared_l2_norm / (2 * stddev * stddev))
  
imports: from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
label_desc: Use image node_id 4 for calling a global function with example usage: exact_gaussian_kernel(x, y, stddev) and returns: math_ops