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 __call__(self, text_input_list, batch_size=32):
      model_device = next(self.model.parameters()).device
      ids = self.tokenizer(text_input_list)
      ids = torch.tensor(ids).to(model_device)
  
      with torch.no_grad():
          outputs = textattack.shared.utils.batch_model_predict(
              self.model, ids, batch_size=batch_size
          )
  
      return outputs
  
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 3 for calling the PyTorchModelWrapper obj's underlying member method code with example usage: obj.__call__(text_input_list, batch_size) and returns: outputs