How to add Attribute and use after building the model?

I’ve built a neural network model and I’d like to incorporate custom functions, encodeImage and encodeText , for pre-processing data. Ideally, I want these functions to be callable both during model definition and after training (post-build). However, including them directly within the model definition restricts their use to before Just-In-Time (JIT) compilation. Calls made after model building result in the functions being undefined

# The Custom Attributes I wan to add in the Model
    def encode_image(self, image):
      return self.visual(image.type(self.dtype))

    def encode_text(self, text):
      x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]

      x = x + self.positional_embedding.type(self.dtype)
      x = x.permute(1, 0, 2)  # NLD -> LND
      x = self.transformer(x)
      x = x.permute(1, 0, 2)  # LND -> NLD
      x = self.ln_final(x).type(self.dtype)

      # x.shape = [batch_size, n_ctx, transformer.width]
      # take features from the eot embedding (eot_token is the highest number in each sequence)
      x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection

      return x
  # Image Classifier Neural Network
  class ImageClassifier(nn.Module):
      def __init__(self, n_qubits, n_layers, encode_image):
          self.model = nn.Sequential(
      def forward(self, x):
          result = self.model(x)
          return result
  batch_size = 28
      channels = 1
      height = 28
      width = 28
      example_input = torch.randn(height, width)
      traced_model = torch.jit.trace(clf, example_input)
      # Save JIT archive'')
      with open('', 'wb') as f:
          save(clf.state_dict(), f)