class_docstr: An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
  models.
class_docstr_tok: ["An","abstract","class","to","handle","weights","initialization","and","a","simple","interface","for","downloading","and","loading","pretrained","models","."]
code: def input_signature(self):
      # Although GPT-2 supports token_type_ids in theory, in practice they are rarely used, and the implementation
      # means that passing token_type_ids=0 yields different outputs from token_type_ids=None.
      # Therefore, we remove the token_type_ids argument by default, even though it would usually be included.
      return {
          "input_ids": tf.TensorSpec(
              (None, None), tf.int32, name="input_ids"
          ),
          "attention_mask": tf.TensorSpec(
              (None, None), tf.int32, name="attention_mask"
          ),
      }
  
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutputWithPastAndCrossAttentions, TFCausalLMOutputWithCrossAttentions, TFSequenceClassifierOutputWithPast
  from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFConv1D, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, TFSequenceSummary, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
  from ...utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_gpt2 import GPT2Config
label_desc: Use image node_id 1 for calling the TFGPT2PreTrainedModel obj's underlying member method code with example usage: obj.input_signature() and returns: dict