old_contents: import numpy as np
  import six
  
  from chainer import cuda
  from chainer import function
  from chainer.utils import type_check
  
  
  def _as_two_dim(x):
      if x.ndim == 2:
          return x
      return x.reshape((len(x), -1))
  
  
  class BatchL2NormSquared(function.Function):
  
      def check_type_forward(self, in_types):
          type_check.expect(in_types.size() == 1)
          x_type, = in_types
  
          type_check.expect(
              x_type.dtype == np.float32,
              x_type.ndim >= 2,
          )
  
      def forward_cpu(self, inputs):
          x = _as_two_dim(inputs[0])
          return np.array([x[i].dot(x[i]) for i in six.moves.range(len(x))],
                          dtype=np.float32),
  
      def forward_gpu(self, inputs):
          x = _as_two_dim(inputs[0])
          l2normsquared_kernel = cuda.cupy.ReductionKernel(
              'T x', 'T y', 'x * x', 'a + b', 'y = a', '0', 'l2normsquared'
          )
          return l2normsquared_kernel(x, axis=1),
  
      def backward(self, inputs, gy):
          x = _as_two_dim(inputs[0])
          xp = cuda.get_array_module(x, gy)
          return xp.array([2 * x[i] * gy[0][i] for i in six.moves.range(len(x))],
                          dtype=np.float32).reshape(inputs[0].shape),
  
  
  def batch_l2_norm_squared(x):
      """L2 norm (a.k.a. Euclidean norm) squared.
  
      This function implements the square of L2 norm on a vector. No reduction
      along batch axis is done.
  
      Args:
          x (~chainer.Variable): Input variable. The first dimension is assumed
              to be the *minibatch dimension*. If x has more than two dimensions
              all but the first dimension are flattened to one dimension.
  
      Returns:
          ~chainer.Variable: Two dimensional output variable.
  
      """
      return BatchL2NormSquared()(x)
  
new_contents: import numpy as np
  import six
  
  from chainer import cuda
  from chainer import function
  from chainer.utils import type_check
  
  
  def _as_two_dim(x):
      if x.ndim == 2:
          return x
      return x.reshape((len(x), -1))
  
  
  class BatchL2NormSquared(function.Function):
  
      def check_type_forward(self, in_types):
          type_check.expect(in_types.size() == 1)
          x_type, = in_types
  
          type_check.expect(
              x_type.dtype == np.float32,
              x_type.ndim >= 2,
          )
  
      def forward_cpu(self, inputs):
          x = _as_two_dim(inputs[0])
          return np.array([x[i].dot(x[i]) for i in six.moves.range(len(x))],
                          dtype=np.float32),
  
      def forward_gpu(self, inputs):
          x = _as_two_dim(inputs[0])
          l2normsquared_kernel = cuda.cupy.ReductionKernel(
              'T x', 'T y', 'x * x', 'a + b', 'y = a', '0', 'l2normsquared'
          )
          return l2normsquared_kernel(x, axis=1),
  
      def backward_cpu(self, inputs, gy):
          x = _as_two_dim(inputs[0])
          xp = cuda.get_array_module(x, gy)
          return xp.array([2 * x[i] * gy[0][i] for i in six.moves.range(len(x))],
                          dtype=np.float32).reshape(inputs[0].shape),
  
      def backward_gpu(self, inputs, gy):
          x = _as_two_dim(inputs[0])
          gy_repeat = gy[0].reshape(len(gy[0]), 1).repeat(x.shape[1], axis=1)
          kernel = cuda.elementwise(
              'T x, T gy', 'T gx', 'gx = 2 * x * gy', 'l2normsquared_bwd')
          gx = kernel(x, gy_repeat).reshape(inputs[0].shape)
          return gx,
  
  
  def batch_l2_norm_squared(x):
      """L2 norm (a.k.a. Euclidean norm) squared.
  
      This function implements the square of L2 norm on a vector. No reduction
      along batch axis is done.
  
      Args:
          x (~chainer.Variable): Input variable. The first dimension is assumed
              to be the *minibatch dimension*. If x has more than two dimensions
              all but the first dimension are flattened to one dimension.
  
      Returns:
          ~chainer.Variable: Two dimensional output variable.
  
      """
      return BatchL2NormSquared()(x)
  
subject: add backward_gpu