code: def __init__(
      self,
      name: str,
      input_dim: int,
      expected_epochs: List[int],
      validation_keys: List[str],
      test_keys: List[str],
  ):
      super().__init__()
      self.name = name
      self.linear1 = nn.Linear(input_dim, 1)
      self.linear2 = nn.Linear(input_dim, 1)
      self.loss_fn = nn.BCEWithLogitsLoss()
  
      self._call_count = {
          "train": 0,
          "validation": 0,
          "test": 0,
      }
      self.expected_epochs = expected_epochs
      self.validation_keys = validation_keys
      self.test_keys = test_keys
  
imports: import unittest
  from typing import List
  import pytorch_lightning
  import torch
  import torch.nn
  import torch.optim
  from reagent.reporting import CompoundReporter, ReporterBase
  from reagent.training import MultiStageTrainer, ReAgentLightningModule
  from torch.utils.data import DataLoader, TensorDataset
label_desc: Use image node_id 1 to create a new DummyTrainer object from inherited base classes: ReAgentLightningModule with example: obj = DummyTrainer(name, input_dim, expected_epochs, validation_keys, test_keys)