class_docstr: Base class for all models
      
class_docstr_tok: ["Base","class","for","all","models"]
docstr: Model prints with number of trainable parameters
docstr_tok: ["Model","prints","with","number","of","trainable","parameters"]
code: def __str__(self):
      """
      Model prints with number of trainable parameters
      """
      params = sum(
          p.numel() for p in self.parameters() if p.requires_grad
      )
      return super().__str__() + "\nTrainable parameters: {}".format(
          params
      )
  
imports: import torch
  import torch.nn
  from textbox import CLM_MODELS, SEQ2SEQ_MODELS, RNN_MODELS, PLM_MODELS
  from transformers import EncoderDecoderModel
  import os
  from typing import List, Optional, Tuple, Union
  from transformers.modeling_utils import get_parameter_dtype
  from collections import OrderedDict
label_desc: Use image node_id 5 for calling the AbstractModel obj's underlying member method code with example usage: obj.__str__() and returns: unknown