code: def forward(self, _input):
      mlp_output = self.net(_input)
      # The mean parameter is unconstrained
      mu = mlp_output[:, : self.hidden_size]
      # The standard deviation must be positive. Parameterise with a softplus
      sigma = self.softplus(mlp_output[:, self.hidden_size :])
      return mu, sigma
  
imports: import torch
  from torch import nn
  from torch.distributions.multivariate_normal import MultivariateNormal
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the PostEncoderMLP obj's underlying member method code with example usage: obj.forward(_input) and returns: mu,  sigma