class_docstr: Applies the element-wise function:
  
  .. math::
      \text{SSP}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) - \log(\text{shift})
  
  Attributes
  ----------
  beta : int
      :math:`\beta` value for the mathematical formulation. Default to 1.
  shift : int
      :math:`\text{shift}` value for the mathematical formulation. Default to 2.
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code: def __init__(self, beta=1, shift=2, threshold=20):
      super(ShiftedSoftplus, self).__init__()
  
      self.shift = shift
      self.softplus = nn.Softplus(beta=beta, threshold=threshold)
  
file_path: dgl/python/dgl/nn/pytorch/conv/cfconv.py
imports: import numpy
  import torch.nn
  from ....None import function
label_desc: Use image node_id 1 to create a new ShiftedSoftplus object from inherited base classes: nn with example: obj = ShiftedSoftplus(beta, shift, threshold)