code: def prepare_inputs_for_generation(
      self, inputs, past=None, use_cache=None, **kwargs
  ):
      attention_mask = kwargs.get("attention_mask", None)
  
      # only last token for inputs_ids if past is defined in kwargs
      if past:
          inputs = tf.expand_dims(inputs[:, -1], -1)
  
      return {
          "input_ids": inputs,
          "attention_mask": attention_mask,
          "past_key_values": past,
          "use_cache": use_cache,
      }
  
imports: from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast
  from ...modeling_tf_utils import DUMMY_INPUTS, TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSharedEmbeddings, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list, stable_softmax
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_opt import OPTConfig
label_desc: Use image node_id 3 for calling the TFOPTForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(inputs, past, use_cache) and returns: dict