class Linear(Module):
  def __init__(self, in_features, out_features, bias=True):
    super(Linear, self).__init__()
    self.in_features = in_features
    self.out_features = out_features
    self.weight = Parameter(torch.Tensor(out_features, in_features))
    if bias:
      self.bias = Parameter(torch.Tensor(out_features))
    else:
      self.register_parameter('bias', None)
    self.reset_parameters()

  def reset_parameters(self):
    stdv = 1. / math.sqrt(self.weight.size(1))
    self.weight.data.uniform_(-stdv, stdv)
    if self.bias is not None:
      self.bias.data.uniform_(-stdv, stdv)

  def forward(self, input):
    return F.linear(input, self.weight, self.bias)

  def extra_repr(self):
    return 'in_features={}, out_features={}, bias={}'.format(
      self.in_features, self.out_features, self.bias is not None
    )

class Reshape(nn.Module):
  def __init__(self, *args):
    super(Reshape, self).__init__()
    self.shape = args

  def forward(self, x):
    return x.view((x.size(0),)+self.shape)

class LinearWise(nn.Module):
  def __init__(self, in_features, bias=True):
    super(LinearWise, self).__init__()
    self.in_features = in_features

    self.weight = nn.Parameter(torch.Tensor(self.in_features))
    if bias:
      self.bias = nn.Parameter(torch.Tensor(self.in_features))
    else:
      self.register_parameter('bias', None)
    self.reset_parameters()

  def reset_parameters(self):
    stdv = 1. / math.sqrt(self.weight.size(0))
    self.weight.data.uniform_(-stdv, stdv)
    if self.bias is not None:
      self.bias.data.uniform_(-stdv, stdv)

  def forward(self, input):
    x = input * self.weight
    if self.bias is not None:
      x = x + self.bias
    return x