code: def setUp(self):
      self.evadb = get_evadb_for_testing()
      self.evadb.catalog().reset()
  
      # Prepare needed Functions and data_col.
      load_functions_for_testing(self.evadb, mode="debug")
      self.img_path = create_sample_image()
  
      # Create base comparison table.
      create_table_query = """CREATE TABLE IF NOT EXISTS testSimilarityTable
                                    (data_col NDARRAY UINT8(3, ANYDIM, ANYDIM),
                                     dummy INTEGER);"""
      execute_query_fetch_all(self.evadb, create_table_query)
  
      # Create feature table.
      create_table_query = """CREATE TABLE IF NOT EXISTS testSimilarityFeatureTable
                                    (feature_col NDARRAY FLOAT32(1, ANYDIM),
                                     dummy INTEGER);"""
      execute_query_fetch_all(self.evadb, create_table_query)
  
      # Prepare injected data_col.
      base_img = np.array(np.ones((3, 3, 3)), dtype=np.uint8)
      base_img[0] -= 1
      base_img[2] += 1
  
      # id: 1 -> most dissimilar, id: 5 -> most similar
      base_img += 4
  
      # Inject data_col.
      base_table_catalog_entry = (
          self.evadb.catalog().get_table_catalog_entry(
              "testSimilarityTable"
          )
      )
      feature_table_catalog_entry = (
          self.evadb.catalog().get_table_catalog_entry(
              "testSimilarityFeatureTable"
          )
      )
  
      # Image list.
      self.img_path_list = []
  
      storage_engine = StorageEngine.factory(
          self.evadb, base_table_catalog_entry
      )
      for i in range(5):
          storage_engine.write(
              base_table_catalog_entry,
              Batch(
                  pd.DataFrame(
                      [
                          {
                              "data_col": base_img,
                              "dummy": i,
                          }
                      ]
                  )
              ),
          )
          storage_engine.write(
              feature_table_catalog_entry,
              Batch(
                  pd.DataFrame(
                      [
                          {
                              "feature_col": base_img.astype(
                                  np.float32
                              ).reshape(1, -1),
                              "dummy": i,
                          }
                      ]
                  )
              ),
          )
  
          # Create an actual image dataset.
          img_save_path = os.path.join(
              self.evadb.catalog().get_configuration_catalog_value(
                  "tmp_dir"
              ),
              f"test_similar_img{i}.jpg",
          )
          try_to_import_cv2()
          import cv2
  
          cv2.imwrite(img_save_path, base_img)
          load_image_query = f"LOAD IMAGE '{img_save_path}' INTO testSimilarityImageDataset;"
          execute_query_fetch_all(self.evadb, load_image_query)
  
          self.img_path_list.append(img_save_path)
  
          base_img -= 1
  
      # Set the env variables.
      self.original_pinecone_key = os.environ.get("PINECONE_API_KEY")
      self.original_pinecone_env = os.environ.get("PINECONE_ENV")
  
      os.environ[
          "PINECONE_API_KEY"
      ] = "657e4fae-7208-4555-b0f2-9847dfa5b818"
      os.environ["PINECONE_ENV"] = "gcp-starter"
  
      self.original_milvus_uri = os.environ.get("MILVUS_URI")
      self.original_milvus_db_name = os.environ.get("MILVUS_DB_NAME")
  
      os.environ["MILVUS_URI"] = "http://localhost:19530"
      # use default Milvus database for testing
      os.environ["MILVUS_DB_NAME"] = "default"
  
      self.original_weaviate_key = os.environ.get("WEAVIATE_API_KEY")
      self.original_weaviate_env = os.environ.get("WEAVIATE_API_URL")
  
      os.environ[
          "WEAVIATE_API_KEY"
      ] = "NM4adxLmhtJDF1dPXDiNhEGTN7hhGDpymmO0"
      os.environ[
          "WEAVIATE_API_URL"
      ] = "https://cs6422-test2-zn83syib.weaviate.network"
  
imports: import os
  import time
  import unittest
  from test.markers import chromadb_skip_marker, milvus_skip_marker, pinecone_skip_marker, qdrant_skip_marker, weaviate_skip_marker
  from test.util import create_sample_image, get_evadb_for_testing, load_functions_for_testing, shutdown_ray
  import numpy
  import pandas
  import pytest
  from evadb.models.storage.batch import Batch
  from evadb.server.command_handler import execute_query_fetch_all
  from evadb.storage.storage_engine import StorageEngine
  from evadb.utils.generic_utils import try_to_import_cv2
label_desc: Use image node_id 1 for calling the SimilarityTests obj's underlying member method code with example usage: obj.setUp() without return types