code: def test_same_vector(self):
      vocab = Vocabulary().add_word_lst(["The", "the", "THE", "a", "A"])
      embed = StaticEmbedding(
          vocab, model_dir_or_name=None, embedding_dim=5, lower=True
      )
      words = torch.LongTensor(
          [
              [
                  vocab.to_index(word)
                  for word in ["The", "the", "THE", "a", "A"]
              ]
          ]
      )
      words = embed(words)
      embed_0 = words[0, 0]
      for i in range(1, 3):
          assert torch.sum(embed_0 == words[0, i]).eq(len(embed_0))
      embed_0 = words[0, 3]
      for i in range(3, 5):
          assert torch.sum(embed_0 == words[0, i]).eq(len(embed_0))
  
file_path: fastNLP/tests/embeddings/torch/test_static_embedding.py
imports: import pytest
  import os
  from fastNLP.embeddings.torch import StaticEmbedding
  from fastNLP import Vocabulary
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  import numpy
label_desc: Use image node_id 1 for calling the TestRandomSameEntry obj's underlying member method code with example usage: obj.test_same_vector() without return types