code: def exact_laplacian_kernel(x, y, stddev):
      r"""Computes exact Laplacian kernel value(s) for tensors x and y using stddev.
  
      The Laplacian kernel for vectors u, v is defined as follows:
           K(u, v) = exp(-||u-v|| / stddev)
      where the norm is the l1-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 Laplacian 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_l1_norm = math_ops.reduce_sum(
          math_ops.abs(math_ops.subtract(x_aligned, y_aligned)), 2
      )
      return math_ops.exp(-diff_l1_norm / stddev)
  
imports: from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
label_desc: Use image node_id 5 for calling a global function with example usage: exact_laplacian_kernel(x, y, stddev) and returns: math_ops