class_docstr: Loads a PyTorch model (`nn.Module`) and tokenizer.
  
  Args:
      model (torch.nn.Module): PyTorch model
      tokenizer: tokenizer whose output can be packed as a tensor and passed to the model.
          No type requirement, but most have `tokenizer` method that accepts list of strings.
class_docstr_tok: ["Loads","a","PyTorch","model","(","`","nn.Module","`",")","and","tokenizer",".","Args",":","model","(","torch.nn.Module",")",":","PyTorch","model","tokenizer",":","tokenizer","whose","output","can","be","packed","as","a","tensor","and","passed","to","the","model",".","No","type","requirement",",","but","most","have","`","tokenizer","`","method","that","accepts","list","of","strings","."]
docstr: Helper method that for `tokenize`
  Args:
      inputs (list[str]): list of input strings
  Returns:
      tokens (list[list[str]]): List of list of tokens as strings
docstr_tok: ["Helper","method","that","for","`","tokenize","`","Args",":","inputs","(","list","[","str","]",")",":","list","of","input","strings","Returns",":","tokens","(","list","[","list","[","str","]","]",")",":","List","of","list","of","tokens","as","strings"]
code: def _tokenize(self, inputs):
      """Helper method that for `tokenize`
      Args:
          inputs (list[str]): list of input strings
      Returns:
          tokens (list[list[str]]): List of list of tokens as strings
      """
      return [
          self.tokenizer.convert_ids_to_tokens(self.tokenizer(x))
          for x in inputs
      ]
  
file_path: TextAttack/textattack/models/wrappers/pytorch_model_wrapper.py
imports: import torch
  from torch.nn import CrossEntropyLoss
  import textattack
  from .model_wrapper import ModelWrapper
label_desc: Use image node_id 5 for calling the PyTorchModelWrapper obj's underlying member method code with example usage: obj._tokenize(inputs) and returns: unknown