code: def prepare_inputs_for_generation(
      self, inputs, past_key_values=None, use_cache=None, **kwargs
  ):
      token_type_ids = kwargs.get("token_type_ids", None)
      # only last token for inputs_ids if past is defined in kwargs
      if past_key_values:
          inputs = tf.expand_dims(inputs[:, -1], -1)
          if token_type_ids is not None:
              token_type_ids = tf.expand_dims(token_type_ids[:, -1], -1)
  
      position_ids = kwargs.get("position_ids", None)
      attention_mask = kwargs.get("attention_mask", None)
  
      if attention_mask is not None and position_ids is None:
          position_ids = tf.math.cumsum(
              attention_mask, axis=-1, exclusive=True
          )
          if past_key_values:
              position_ids = tf.expand_dims(position_ids[:, -1], -1)
  
      return {
          "input_ids": inputs,
          "attention_mask": attention_mask,
          "position_ids": position_ids,
          "past_key_values": past_key_values,
          "use_cache": use_cache,
          "token_type_ids": token_type_ids,
      }
  
imports: from __future__ import annotations
  from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutputWithPast
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFSharedEmbeddings, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
  from ...utils import logging
  from .configuration_gptj import GPTJConfig
label_desc: Use image node_id 4 for calling the TFGPTJForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(inputs, past_key_values, use_cache) and returns: dict