code: def __init__(
      self,
      parent,
  ):
      self.parent = parent
      self.batch_size = 13
      self.seq_length = 7
      self.is_training = True
      self.use_input_mask = True
      self.use_token_type_ids = True
      self.use_labels = True
      self.vocab_size = 99
      self.hidden_size = 32
      self.num_hidden_layers = 5
      self.num_attention_heads = 4
      self.intermediate_size = 37
      self.hidden_act = "gelu"
      self.hidden_dropout_prob = 0.1
      self.attention_probs_dropout_prob = 0.1
      self.max_position_embeddings = 512
      self.type_vocab_size = 16
      self.type_sequence_label_size = 2
      self.initializer_range = 0.02
      self.num_labels = 3
      self.num_choices = 4
      self.scope = None
      self.attention_window = 4
  
      # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
      # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
      # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
      # because its local attention only attends to `self.attention_window` and one before and one after
      self.key_length = self.attention_window + 2
  
imports: import unittest
  from transformers import is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow, tooslow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 1 to create a new TFLongformerModelTester object  with example: obj = TFLongformerModelTester(parent)