class_docstr: Computes a parameterised scalar mixture of :math:`N` tensors, :math:`mixture = \gamma * \sum_{k}(s_k * tensor_k)`
  where :math:`s = \mathrm{softmax}(w)`, with :math:`w` and :math:`\gamma` scalar parameters.
  
  Args:
      n_layers (int):
          The number of layers to be mixed, i.e., :math:`N`.
      dropout (float):
          The dropout ratio of the layer weights.
          If dropout > 0, then for each scalar weight, adjust its softmax weight mass to 0
          with the dropout probability (i.e., setting the unnormalized weight to -inf).
          This effectively redistributes the dropped probability mass to all other weights.
          Default: 0.
code: def __repr__(self):
      s = f"n_layers={self.n_layers}"
      if self.dropout.p > 0:
          s += f", dropout={self.dropout.p}"
  
      return f"{self.__class__.__name__}({s})"
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the ScalarMix obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, s, str