code: def __init__(self, d_hidden: int, d_data: int):
      super().__init__()
      self.mean = nn.Linear(d_hidden, d_data)
      self.var = nn.Sequential(
          nn.Linear(d_hidden, d_data), PositiveSoftplus(1e-3)
      )
  
file_path: gluonts/src/gluonts/nursery/daf/tslib/nn/distributions.py
imports: from typing import List, Tuple
  import torch
  from torch import Tensor
  from torch import nn
  from torch.distributions import Distribution
  from .activations import PositiveSoftplus
label_desc: Use image node_id 1 to create a new GaussianLayer object from inherited base classes: nn with example: obj = GaussianLayer(d_hidden, d_data)