code: def __init__(self, data: DatasetSplits, hidden_dim: int):
      super(TestModel, self).__init__()
  
      self.input_dim = len(data.vectorizer.data_vocab)
      self.output_dim = len(data.vectorizer.target_vocab)
      self.hidden_dim = hidden_dim
  
      self.fc = torch.nn.Linear(
          in_features=self.input_dim, out_features=hidden_dim
      )
      self.fc2 = torch.nn.Linear(
          in_features=hidden_dim, out_features=self.output_dim
      )
  
imports: import numpy
  import pandas
  import torch
  from transfer_nlp.common.tokenizers import CharacterTokenizer
  from transfer_nlp.loaders.loaders import DatasetSplits, DataFrameDataset
  from transfer_nlp.loaders.vectorizers import Vectorizer
  from transfer_nlp.loaders.vocabulary import Vocabulary
  from transfer_nlp.plugins.config import register_plugin
  from transfer_nlp.plugins.helpers import ObjectHyperParams
label_desc: Use image node_id 1 to create a new TestModel object from inherited base classes: torch.nn with example: obj = TestModel(data, hidden_dim)