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","."]
code: def to(self, device):
      self.model.to(device)
  
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 2 for calling the PyTorchModelWrapper obj's underlying member method code with example usage: obj.to(device) without return types