code: def test_global_attention_mask(self):
      for tokenizer in [
          self.default_tokenizer,
          self.default_tokenizer_fast,
      ]:
          src_text = ["Summary of the text.", "Another summary."]
          expected_global_attention_mask = [
              [0, 0, 0, 0, 0, 0, 0],
              [0, 0, 0, 0, 0, -1, -1],
          ]
  
          encoded_output = tokenizer(src_text, padding=False)
          encoded_output["global_attention_mask"] = [
              [0] * len(x) for x in encoded_output["input_ids"]
          ]
          outputs = tokenizer.pad(encoded_output)
          self.assertSequenceEqual(
              outputs["global_attention_mask"],
              expected_global_attention_mask,
          )
  
imports: import json
  import os
  import unittest
  from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
  from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  from ...test_tokenization_common import TokenizerTesterMixin
label_desc: Use image node_id 12 for calling the TestTokenizationLED obj's underlying member method code with example usage: obj.test_global_attention_mask() without return types