code: def setUp(self):
      self.batch_size = 10
      self.num_tokens = 20
      self.embedding_dim = 1024
      self.vocab_size = 1000
      self.padding_idx = 0
      self.num_encoder_layers = 6
  
      # Generate a tensor of token ids as input tokens
      self.tokens = (
          torch.randint(5, 1000, (self.batch_size, self.num_tokens))
      ).long()
      self.lengths = torch.tensor([self.num_tokens])
      self.pad_mask = (
          torch.ones(self.batch_size, self.num_tokens)
      ).long()
      self.segment_labels = (
          torch.ones(self.batch_size, self.num_tokens)
      ).long()
      self.positions = None
  
imports: import unittest
  import torch
  from pytext.models.representations.transformer_sentence_encoder import TransformerSentenceEncoder
label_desc: Use image node_id 1 for calling the TransformerSentenceEncoderTest obj's underlying member method code with example usage: obj.setUp() without return types