code: def __init__(self, num_inputs, num_outputs):
      super().__init__()
  
      init_ = lambda m: init(
          m, init_normc_, lambda x: torch.nn.init.constant_(x, 0)
      )
  
      self.fc_mean = init_(torch.nn.Linear(num_inputs, num_outputs))
      self.logstd = AddBias(torch.zeros(num_outputs))
  
file_path: SerpentAI/serpent/machine_learning/reinforcement_learning/ppo/distributions.py
imports: import math
  import torch
label_desc: Use image node_id 1 to create a new DiagGaussian object from inherited base classes: torch.nn with example: obj = DiagGaussian(num_inputs, num_outputs)