code: def get_causal_mask(self, key_length, query_length) -> tf.Tensor:
      return tf.cast(
          self.lower_triangle_mask[
              :, :, key_length - query_length : key_length, :key_length
          ],
          tf.bool,
      )
  
imports: from typing import Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...file_utils import DUMMY_INPUTS, 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 shape_list, stable_softmax
  from ...utils import logging
  from .configuration_gptj import GPTJConfig
label_desc: Use image node_id 2 for calling the TFGPTJAttention obj's underlying member method code with example usage: obj.get_causal_mask(key_length, query_length) and returns: tf