code: def __init__(self, weights=None):
      super().__init__()
  
      self.training_step_called = False
      self.validation_step_called = False
  
      self.assert_backward = True
  
      self.l1 = nn.Linear(2, 3, bias=False)
      if weights is None:
          weights = torch.tensor([[4, 3, 5], [10, 11, 13]]).float()
          p = torch.nn.Parameter(weights, requires_grad=True)
          self.l1.weight = p
  
imports: import torch
  from lightning.pytorch.core.module import LightningModule
  from torch import Tensor, nn
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 1 to create a new DeterministicModel object from inherited base classes: LightningModule with example: obj = DeterministicModel(weights)