code: def forward(
      self, x_in: torch.Tensor, apply_softmax: bool = False
  ) -> torch.Tensor:
      intermediate = torch.nn.functional.relu(self.fc(x_in))
      output = self.fc2(intermediate)
  
      if self.output_dim == 1:
          output = output.squeeze()
  
      if apply_softmax:
          output = torch.nn.functional.softmax(output, dim=1)
  
      return output
  
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 2 for calling the TestModel obj's underlying member method code with example usage: obj.forward(x_in, apply_softmax) and returns: output