class_docstr: Base class for all models
      
class_docstr_tok: ["Base","class","for","all","models"]
docstr: Predict the texts conditioned on a noise or sequence.
  
  Args:
      batch_data (Corpus): Corpus class of a single batch.
  
  Returns:
      torch.Tensor: Generated text, shape: [batch_size, max_len]
docstr_tok: ["Predict","the","texts","conditioned","on","a","noise","or","sequence",".","Args",":","batch_data","(","Corpus",")",":","Corpus","class","of","a","single","batch",".","Returns",":","torch.Tensor",":","Generated","text",",","shape",":","[","batch_size",",","max_len","]"]
code: def generate(self, batch_data):
      r"""Predict the texts conditioned on a noise or sequence.
  
      Args:
          batch_data (Corpus): Corpus class of a single batch.
  
      Returns:
          torch.Tensor: Generated text, shape: [batch_size, max_len]
      """
      raise NotImplementedError
  
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 3 for calling the AbstractModel obj's underlying member method code with example usage: obj.generate(batch_data) without return types