old_contents: import chainerx
  
  
  def relu(a):
      """Rectified Linear Unit function.
  
  Args:
      a (~chainerx.ndarray): Input array.
  
  Returns:
      :class:`~chainerx.ndarray`: Returned array: :math:`y = \\max (0, x)`.
  
  Note:
      During backpropagation, this function propagates the gradient of the
      output array to the input array ``a``.
  """
      return chainerx.maximum(0, a)
  
  
  def sigmoid(a):
      """Element-wise sigmoid logistic function.
  
  Args:
      a (~chainerx.ndarray): Input array.
  
  Returns:
      :class:`~chainerx.ndarray`:
          Returned array: :math:`y = (1 + \\exp (-x))^{-1}`.
  
  Note:
      During backpropagation, this function propagates the gradient of the
      output array to the input array ``a``.
  """
      # TODO(imanishi): Support C++ implementation
      return (chainerx.tanh(a / 2.0) + 1.0) / 2.0
  
new_contents: import chainerx
  
  
  def relu(a):
      """Rectified Linear Unit function.
  
  Args:
      a (~chainerx.ndarray): Input array.
  
  Returns:
      :class:`~chainerx.ndarray`: Returned array: :math:`y = \\max (0, x)`.
  
  Note:
      During backpropagation, this function propagates the gradient of the
      output array to the input array ``a``.
  """
      # TODO(imanishi): The function should also be available to C++ users
      return chainerx.maximum(0, a)
  
  
  def sigmoid(a):
      """Element-wise sigmoid logistic function.
  
  Args:
      a (~chainerx.ndarray): Input array.
  
  Returns:
      :class:`~chainerx.ndarray`:
          Returned array: :math:`y = (1 + \\exp (-x))^{-1}`.
  
  Note:
      During backpropagation, this function propagates the gradient of the
      output array to the input array ``a``.
  """
      # TODO(imanishi): The function should also be available to C++ users
      return (chainerx.tanh(a / 2.0) + 1.0) / 2.0
  
subject: Add todo comment