text: from chainer import backend
  from chainer.backends import cuda
  from chainer import function_node
  import chainer.functions
  from chainer import utils
  from chainer.utils import type_check
  
  
  class Minimum(function_node.FunctionNode):
      """Element-wise minimum of input variables."""
  
      def check_type_forward(self, in_types):
          type_check._argname(in_types, ('x1', 'x2'))
          type_check.expect(
              in_types[0].dtype.kind == 'f',
              in_types[0].dtype == in_types[1].dtype,
          )
          type_check.expect_broadcast_shapes(
              in_types[0].shape, in_types[1].shape)
  
      def forward(self, inputs):
          # may broadcast
          self.retain_inputs((0, 1))
          x1, x2 = inputs
          xp = backend.get_array_module(x1, x2)
          return utils.force_array(xp.minimum(x1, x2)),
  
      def backward(self, indexes, grad_outputs):
          x1, x2 = self.get_retained_inputs()
          return MinimumGrad(x1.data, x2.data).apply((grad_outputs[0],))
  
  
  class MinimumGrad(function_node.FunctionNode):
  
      def __init__(self, x1, x2):
          self.x1 = x1
          self.x2 = x2
  
      def forward_cpu(self, inputs):
          gy, = inputs
          x1, x2 = self.x1, self.x2
          gx1 = utils.force_array(gy * (x1 <= x2))
          gx2 = utils.force_array(gy * (x1 > x2))
          return utils.sum_to(gx1, x1.shape), utils.sum_to(gx2, x2.shape)
  
      def forward_gpu(self, inputs):
          gy, = inputs
          x1, x2 = self.x1, self.x2
          gx1 = cuda.elementwise(
              'T x1, T x2, T gy', 'T gx1',
              'gx1 = (x1 <= x2) ? gy : (T)0.0',
              'minimum_bwd1')(x1, x2, gy)
          gx2 = cuda.elementwise(
              'T x1, T x2, T gy', 'T gx1',
              'gx1 = (x1 > x2) ? gy : (T)0.0',
              'minimum_bwd2')(x1, x2, gy)
          return utils.sum_to(gx1, x1.shape), utils.sum_to(gx2, x2.shape)
  
      def backward(self, indexes, grad_outputs):
          x1, x2 = self.x1, self.x2
          cond = utils.force_array(x1 <= x2)
          ggy = chainer.functions.where(cond, grad_outputs[0], grad_outputs[1])
          return ggy,
  
  
  def minimum(x1, x2):
      """Element-wise minimum of input variables.
  
      Args:
          x1 (:class:`~chainer.Variable` or :ref:`ndarray`):
              Input variables to be compared.
          x2 (:class:`~chainer.Variable` or :ref:`ndarray`):
              Input variables to be compared.
  
      Returns:
          ~chainer.Variable: Output variable.
      """
      return Minimum().apply((x1, x2))[0]
  