code: def _get_search_query_results(self):
      # Get the query feature vector. Create a dummy
      # batch to retreat a single file path.
      dummy_batch = Batch(
          frames=pd.DataFrame(
              {"0": [0]},
          )
      )
      search_batch = self.search_query_expr.evaluate(dummy_batch)
  
      # Scan index. The search batch comes from the Open call.
      feature_col_name = self.search_query_expr.output_objs[0].name
      search_batch.drop_column_alias()
      search_feat = search_batch.column_as_numpy_array(
          feature_col_name
      )[0]
      search_feat = search_feat.reshape(1, -1)
      return search_feat
  
imports: from typing import Iterator
  import pandas
  from evadb.catalog.models.utils import VectorStoreType
  from evadb.catalog.sql_config import ROW_NUM_COLUMN
  from evadb.database import EvaDBDatabase
  from evadb.executor.abstract_executor import AbstractExecutor
  from evadb.executor.executor_utils import ExecutorError, handle_vector_store_params
  from evadb.models.storage.batch import Batch
  from evadb.plan_nodes.storage_plan import StoragePlan
  from evadb.plan_nodes.vector_index_scan_plan import VectorIndexScanPlan
  from evadb.third_party.databases.interface import get_database_handler
  from evadb.third_party.vector_stores.types import VectorIndexQuery
  from evadb.third_party.vector_stores.utils import VectorStoreFactory
  from evadb.utils.logging_manager import logger
label_desc: Use image node_id 3 for calling the VectorIndexScanExecutor obj's underlying member method code with example usage: obj._get_search_query_results() and returns: search_feat