code: def encode(self, text: str, return_tensors: Optional[str] = None):
      tokens = text.split(" ")
      tokens_ids = [
          ord(s[0]) if len(s) == 1 else self.unk for s in tokens
      ]
  
      # start with sos
      tokens_ids: Union[List[int], Tensor] = [self.sos, *tokens_ids]
  
      if return_tensors:
          tokens_ids = torch.tensor([tokens_ids])
  
      return tokens_ids
  
imports: import copy
  from collections import namedtuple
  from typing import cast, List, Optional, Union
  import torch
  from captum.attr._core.feature_ablation import FeatureAblation
  from captum.attr._core.kernel_shap import KernelShap
  from captum.attr._core.layer.layer_integrated_gradients import LayerIntegratedGradients
  from captum.attr._core.lime import Lime
  from captum.attr._core.llm_attr import LLMAttribution, LLMGradientAttribution
  from captum.attr._core.shapley_value import ShapleyValueSampling
  from captum.attr._utils.interpretable_input import TextTemplateInput, TextTokenInput
  from parameterized import parameterized, parameterized_class
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from torch import nn, Tensor
label_desc: Use image node_id 1 for calling the DummyTokenizer obj's underlying member method code with example usage: obj.encode(text, return_tensors) and returns: tokens_ids