class_docstr: Class providing an artificial backend, that reads data into memory.
class_docstr_tok: ["Class","providing","an","artificial","backend",",","that","reads","data","into","memory","."]
docstr: Intersection between data and dataloader elements.
  
  Works only when dataloader is of type `NoBackend`.
docstr_tok: ["Intersection","between","data","and","dataloader","elements",".","Works","only","when","dataloader","is","of","type","`","NoBackend","`","."]
code: def _intersect(self, dataloader):
      """Intersection between data and dataloader elements.
  
      Works only when dataloader is of type `NoBackend`.
      """
      if not isinstance(dataloader.backend, NoBackend):
          msg = "Intersection can only be calculated between same backends (NoBackend), \
              instead get {}".format(
              type(dataloader.backend)
          )
          logger.error(msg)
          raise Exception(msg)
      self.data = np.ascontiguousarray(self.data, dtype="int64")
      dataloader.backend.data = np.ascontiguousarray(
          dataloader.backend.data, dtype="int64"
      )
      av = self.data.view([("", self.data.dtype)] * self.data.shape[1])
      bv = dataloader.backend.data.view(
          [("", dataloader.backend.data.dtype)]
          * dataloader.backend.data.shape[1]
      )
      intersection = (
          np.intersect1d(av, bv)
          .view(self.data.dtype)
          .reshape(
              -1,
              self.data.shape[
                  0 if self.data.flags["F_CONTIGUOUS"] else 1
              ],
          )
      )
      return intersection
  
imports: import logging
  import tempfile
  import uuid
  from datetime import datetime
  import numpy
  import pandas
  import tensorflow
  from .data_indexer import DataIndexer
  from .source_identifier import DataSourceIdentifier
label_desc: Use image node_id 13 for calling the NoBackend obj's underlying member method code with example usage: obj._intersect(dataloader) and returns: intersection