def test_search_with_custom_vector_field_normalize_embeddings_true(self):
        """
        Tensor search for a document in an index where normalize_embeddings was set to true at the time of index creation.

        This test method adds documents with custom vector fields to the index and performs tensor searches.
        It verifies that the search results and highlights are as expected when `normalize_embeddings` is set to True
        during index creation.

        The test covers the following scenarios:
        1. Adding documents with custom vector fields to both structured and unstructured indexes.
        2. Performing tensor searches with context matching custom vectors.
        4. Verifying that searching with normal text returns the expected text.

        The test uses the `self.subTest` context manager to create subtests for different index types.
        """
        for index in [self.structured_custom_index, self.semi_structured_custom_index]:
            with self.subTest(f"Index: {index.name}, type: {index.type}"):
                self.add_documents(
                    config=self.config, add_docs_params=AddDocsParams(
                        index_name=index.name,
                        docs=[
                            {
                                "_id": "custom_vector_doc",
                                "my_custom_vector": {
                                    "content": "custom content is here!!",
                                    "vector": self.random_vector_1  # size is 512
                                }
                            },
                            {
                                "_id": "empty_content_custom_vector_doc",
                                "my_custom_vector": {
                                    "vector": self.random_vector_2  # size is 512
                                }
                            },
                            {
                                "_id": "normal_doc",
                                "text_field": "blah"
                            }
                        ],
                        device="cpu",
                        mappings=self.mappings if isinstance(index, UnstructuredMarqoIndex) else None,
                        tensor_fields=["my_custom_vector", "text_field"] if isinstance(index, UnstructuredMarqoIndex) else None
                    )
                )

                # Searching with context matching custom vector returns custom vector
                res = tensor_search.search(
                    config=self.config, index_name=index.name, text={"dummy text": 0},
                    search_method=enums.SearchMethod.TENSOR,
                    context=SearchContext(**{"tensor": [{"vector": self.random_vector_1, "weight": 1}], })
                )
                assert res["hits"][0]["_id"] == "custom_vector_doc"
                assert res["hits"][0]["_score"] == 1.0
                assert res["hits"][0]["_highlights"][0]["my_custom_vector"] == "custom content is here!!"

                # Tensor search should work even if content is empty (highlight is empty string)
                res = tensor_search.search(
                    config=self.config, index_name=index.name, text={"dummy text": 0},
                    search_method=enums.SearchMethod.TENSOR,
                    context=SearchContext(**{"tensor": [{"vector": self.random_vector_2, "weight": 1}], })
                )
                assert res["hits"][0]["_id"] == "empty_content_custom_vector_doc"
                assert res["hits"][0]["_score"] == 1.0
                assert res["hits"][0]["_highlights"][0]["my_custom_vector"] == ""

                # Searching with normal text returns text
                res = tensor_search.search(
                    config=self.config, index_name=index.name, text="blah",
                    search_method=enums.SearchMethod.TENSOR
                )
                assert res["hits"][0]["_id"] == "normal_doc"

        Tensor search for a document in an index where normalize_embeddings was set to true at the time of index creation.

        This test method adds documents with custom vector fields to the index and performs tensor searches.
        It verifies that the search results and highlights are as expected when `normalize_embeddings` is set to True
        during index creation.

        The test covers the following scenarios:
        1. Adding documents with custom vector fields to both structured and unstructured indexes.
        2. Performing tensor searches with context matching custom vectors.
        4. Verifying that searching with normal text returns the expected text.

        The test uses the `self.subTest` context manager to create subtests for different index types.
        
tests/integ_tests/tensor_search/integ_tests/test_custom_vector_field.py