Review changes

  >>> output_tensor = layer(input_tensor)

  """

<<<<<<< HEAD

  def __init__(self, scale: int, eps: float = 1e-5):

>>>>>>> Type annotations

=======

  def __init__(self, scale: float, eps: float = 1e-5):

>>>>>>> Review changes

    """Initialize a ScaleNorm layer.

    Parameters

  """

  def __init__(self,

               *,

               d_input: int,

               d_hidden: int = None,

               d_output: int = None,

               d_hidden: int,

               d_output: int,

               activation: str,

               n_layers: int,

               dropout_p: float):

    ----------

    d_input: int

      Size of input layer.

    d_hidden: int

    d_hidden: int (same as d_input if d_output = 0)

      Size of hidden layer.

    d_output: int

    d_output: int (same as d_input if d_output = 0)

      Size of output layer.

    activation: str

      Activation function to be used. Can choose between 'relu' for ReLU, 'leakyrelu' for LeakyReLU, 'prelu' for PReLU,

      self.activation = lambda x: x

    self.n_layers = n_layers

    d_output = d_output if d_output is not None else d_input

    d_hidden = d_hidden if d_hidden is not None else d_input

    d_output = d_output if d_output is not 0 else d_input

    d_hidden = d_hidden if d_hidden is not 0 else d_input

    if n_layers == 1:

      self.linears = [nn.Linear(d_input, d_output)]

    x: torch.Tensor

      Input tensor.

    """

    if self.n_layers == 0:

    if not self.n_layers:

      return x

    elif self.n_layers == 1:

    if self.n_layers == 1:

      return self.dropout_p[0](self.act_func(self.linears[0](x)))

    else: