Updated Docs

class ScaleNorm(nn.Module):

  """Apply Scale Normalization to input.

    All G values are initialized to sqrt(d).

  The ScaleNorm layer first computes the square root of the scale, then computes the matrix/vector norm of the input tensor.

  The norm value is calculated as `sqrt(scale) / matrix norm`. 

  Finally, the result is returned as `input_tensor * norm value`.

  References

  ----------

  .. [1] Lukasz Maziarka et al. "Molecule Attention Transformer" Graph Representation Learning workshop and Machine Learning and the Physical Sciences workshop at NeurIPS 2019. 2020. https://arxiv.org/abs/2002.08264

  Examples

  --------

  >>> import deepchem as dc

  >>> scale = 0.35

  >>> layer = dc.models.torch_models.ScaleNorm(scale)

  >>> input_tensor = torch.tensor([[1.269, 39.36], [0.00918, -9.12]])

  >>> output_tensor = layer.forward(input_tensor)

  """

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