code: def __init__(self, input_size, hidden_size):
      super().__init__()
      self.hidden_size = hidden_size
      modules = [
          nn.Linear(input_size, hidden_size),  # Hidden Layer
          nn.Tanh(),
          nn.Linear(hidden_size, hidden_size * 2),
      ]  # Output layer twice the size for mean and variance
      self.net = nn.Sequential(*modules)
      self.softplus = nn.Softplus()
  
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 1 to create a new PostEncoderMLP object from inherited base classes: nn with example: obj = PostEncoderMLP(input_size, hidden_size)