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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docstr: Description
  -----------
  Applies the activation function.
  
  Parameters
  ----------
  inputs : float32 tensor of shape (N, *)
      * denotes any number of additional dimensions.
  
  Returns
  -------
  float32 tensor of shape (N, *)
      Result of applying the activation function to the input.
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code: def forward(self, inputs):
      """
  
      Description
      -----------
      Applies the activation function.
  
      Parameters
      ----------
      inputs : float32 tensor of shape (N, *)
          * denotes any number of additional dimensions.
  
      Returns
      -------
      float32 tensor of shape (N, *)
          Result of applying the activation function to the input.
      """
      return self.softplus(inputs) - np.log(float(self.shift))
  
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 2 for calling the ShiftedSoftplus obj's underlying member method code with example usage: obj.forward(inputs) and returns: unknown