code: def __init__(
      self,
      prediction_length: int,
      context_length: int,
  ) -> None:
      super().__init__()
      assert prediction_length > 0
      assert context_length > 0
      self.prediction_length = prediction_length
      self.context_length = context_length
      self.net = nn.Linear(context_length, prediction_length)
      torch.nn.init.uniform_(self.net.weight, -1.0, 1.0)
  
file_path: gluonts/test/torch/model/test_torch_predictor.py
imports: import tempfile
  from pathlib import Path
  import torch
  import torch.nn
  from gluonts.core.component import validated
  from gluonts.dataset.field_names import FieldName
  from gluonts.model.predictor import Predictor
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.transform import InstanceSplitter, TestSplitSampler
label_desc: Use image node_id 1 to create a new RandomNetwork object from inherited base classes: nn with example: obj = RandomNetwork(prediction_length, context_length)