code: def squared_error(output, target, name=""):
      """
      This operation computes the sum of the squared difference between elements
      in the two input matrices. The result is a scalar (i.e., one by one matrix).
      This is often used as a training criterion.
  
      Example:
          >>> i1 = C.input_variable((1,2))
          >>> i2 = C.input_variable((1,2))
          >>> C.squared_error(i1,i2).eval({i1:np.asarray([[[2., 1.]]], dtype=np.float32), i2:np.asarray([[[4., 6.]]], dtype=np.float32)})
          array([ 29.], dtype=float32)
  
          >>> C.squared_error(i1,i2).eval({i1:np.asarray([[[1., 2.]]], dtype=np.float32), i2:np.asarray([[[1., 2.]]], dtype=np.float32)})
          array([ 0.], dtype=float32)
  
      Args:
          output: the output values from the network
          target: it is usually a one-hot vector where the hot bit
           corresponds to the label index
          name (str, optional): the name of the Function instance in the network
      Returns:
          :class:`~cntk.ops.functions.Function`
      """
      from cntk.cntk_py import squared_error
  
      dtype = get_data_type(output, target)
      output = sanitize_input(output, dtype)
      target = sanitize_input(target, dtype)
      return squared_error(output, target, name)
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  from ..ops.functions import CloneMethod, Function
  from ..variables import Variable, Parameter, Constant
  from cntk.internal import sanitize_input, sanitize_shape, sanitize_axis, sanitize_dynamic_axes, typemap
  from cntk.internal.utils import get_data_type
  from cntk.cntk_py import sentinel_value_for_auto_select_random_seed
  from cntk import times, softmax, parameter
  import cntk
  from ..axis import Axis
label_desc: Use image node_id 6 for calling a global function with example usage: squared_error(output, target, name) and returns: squared_error