class_docstr: Base class for all models
      
class_docstr_tok: ["Base","class","for","all","models"]
code: def generate(self, batch, accelerator):
      inputs = self.process_generate_inputs(batch)
  
      if self.is_prompt_tuning:
          inputs = self._process_prompt_tuning_input(inputs, batch)
  
      if self.is_casual_model:
          input_ids_len = (
              inputs["input_ids"].shape[1]
              if "input_ids" in inputs
              else inputs["inputs_embeds"].shape[1]
          )
          self.generation_kwargs["max_length"] = (
              self.target_max_length + input_ids_len
          )
  
      # sample_outputs = self.model.generate(**inputs, **self.generation_kwargs)
      sample_outputs = accelerator.unwrap_model(self.model).generate(
          **inputs, **self.generation_kwargs
      )
      sample_outputs = accelerator.pad_across_processes(
          sample_outputs, dim=1, pad_index=self.tokenizer.pad_token_id
      )
      sample_outputs = accelerator.gather((sample_outputs))
  
      if self.is_casual_model:
          sample_outputs = sample_outputs[:, input_ids_len:]
  
      decode_kwargs = {
          "skip_special_tokens": True,
          "clean_up_tokenization_spaces": False,
      }
      generated_text = self.tokenizer.batch_decode(
          sample_outputs, **decode_kwargs
      )
      generated_text = [g.strip() or "NULL" for g in generated_text]
      return generated_text
  
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 7 for calling the AbstractModel obj's underlying member method code with example usage: obj.generate(batch, accelerator) and returns: generated_text