old_contents: import numpy
  
  from chainer.functions.connection import linear
  from chainer import link
  import chainer.initializations as I
  
  
  class Linear(link.Link):
  
      """Linear layer (a.k.a. fully-connected layer).
  
      This is a link that wraps the :func:`~chainer.functions.linear` function,
      and holds a weight matrix ``W`` and optionally a bias vector ``b`` as
      parameters.
  
      The weight matrix ``W`` is initialized with i.i.d. Gaussian samples, each
      of which has zero mean and deviation :math:`\sqrt{1/\\text{in_size}}`. The
      bias vector ``b`` is of size ``out_size``. Each element is initialized with
      the ``bias`` value. If ``nobias`` argument is set to True, then this link
      does not hold a bias vector.
  
      Args:
          in_size (int): Dimension of input vectors.
          out_size (int): Dimension of output vectors.
          wscale (float): Scaling factor of the weight matrix.
          bias (float): Initial bias value.
          nobias (bool): If True, then this function does not use the bias.
          initialW (2-D array): Initial weight value. If ``None``, then this
              function uses to initialize ``wscale``. May also be a function 
              that takes a tuple of (outpu_size, input_size) and returns a 
              matrix of the same dimensions to use for initialization
          initial_bias (1-D array): Initial bias value. If ``None``, then this
              function uses to initialize ``bias``. May also be a function
              that behaves in the same manner as initalW
  
      .. seealso:: :func:`~chainer.functions.linear`
  
      Attributes:
          W (~chainer.Variable): Weight parameter.
          b (~chainer.Variable): Bias parameter.
  
      """
      def __init__(self, in_size, out_size, wscale=1, bias=0, nobias=False,
                   initialW=None, initial_bias=None):
          super(Linear, self).__init__(W=(out_size, in_size))
  
          I.init_weight(self.W.data, initialW, scale=wscale)
  
          if nobias:
              self.b = None
          else:
              self.add_param('b', out_size)
              I.init_weight(self.b.data, initialW, scale=wscale, none_default=bias)
  
  
      def __call__(self, x):
          """Applies the linear layer.
  
          Args:
              x (~chainer.Variable): Batch of input vectors.
  
          Returns:
              ~chainer.Variable: Output of the linear layer.
  
          """
          return linear.linear(x, self.W, self.b)
  
new_contents: import numpy
  
  from chainer.functions.connection import linear
  from chainer import link
  import chainer.initializations as I
  
  
  class Linear(link.Link):
  
      """Linear layer (a.k.a. fully-connected layer).
  
      This is a link that wraps the :func:`~chainer.functions.linear` function,
      and holds a weight matrix ``W`` and optionally a bias vector ``b`` as
      parameters.
  
      The weight matrix ``W`` is initialized with i.i.d. Gaussian samples, each
      of which has zero mean and deviation :math:`\sqrt{1/\\text{in_size}}`. The
      bias vector ``b`` is of size ``out_size``. Each element is initialized with
      the ``bias`` value. If ``nobias`` argument is set to True, then this link
      does not hold a bias vector.
  
      Args:
          in_size (int): Dimension of input vectors.
          out_size (int): Dimension of output vectors.
          wscale (float): Scaling factor of the weight matrix.
          bias (float): Initial bias value.
          nobias (bool): If True, then this function does not use the bias.
          initialW (2-D array): Initial weight value. If ``None``, then this
              function uses to initialize ``wscale``. May also be a function 
              that takes a tuple of (outpu_size, input_size) and returns a 
              matrix of the same dimensions to use for initialization
          initial_bias (1-D array): Initial bias value. If ``None``, then this
              function uses to initialize ``bias``. May also be a function
              that behaves in the same manner as initalW
  
      .. seealso:: :func:`~chainer.functions.linear`
  
      Attributes:
          W (~chainer.Variable): Weight parameter.
          b (~chainer.Variable): Bias parameter.
  
      """
      def __init__(self, in_size, out_size, wscale=1, bias=0, nobias=False,
                   initialW=None, initial_bias=None):
          super(Linear, self).__init__(W=(out_size, in_size))
  
          I.init_weight(self.W.data, initialW, scale=wscale)
  
          if nobias:
              self.b = None
          else:
              self.add_param('b', out_size)
              I.init_weight(self.b.data, initialW, none_default=bias)
  
  
      def __call__(self, x):
          """Applies the linear layer.
  
          Args:
              x (~chainer.Variable): Batch of input vectors.
  
          Returns:
              ~chainer.Variable: Output of the linear layer.
  
          """
          return linear.linear(x, self.W, self.b)
  
subject: remove copy pasta error