TF_DeleteTensor(t);
  EXPECT_EQ(2, product[0]);
  EXPECT_EQ(4, product[1]);
  EXPECT_EQ(6, product[2]);
  EXPECT_EQ(8, product[3]);

  // When we turn on grappler, confirm that the tf.Add has been rewritten into a
  // tf.Mul.
  // This capability of checking the executed op names is currently only enabled
  // for TFRT debug build, for performance and simplicity reasons.

from tensorflow.python.platform import app  # pylint: disable=g-import-not-at-top
app.flags = flags

# These symbols appear because we import the python package which
# in turn imports from tensorflow.core and tensorflow.python. They
# must come from this module. So python adds these symbols for the
# resolution to succeed.
# pylint: disable=undefined-variable
del python
del core

  const std::vector<std::string>& underlying_devices() const {
    return underlying_devices_;
  }

  // Takes a description of a single operation being executed on the
  // ParallelDevice, and in turn runs one operation per component device with
  // its corresponding inputs from the input ParallelTensors. Wraps the
  // resulting per-device and per-output TFE_TensorHandles into one

  /// registration being done in a static instance of `Env`, the destructor of
  /// `FileSystem` is never called (see
  /// https://github.com/tensorflow/tensorflow/issues/27535). In turn, this
  /// function will never be called. There are plans to refactor registration
  /// and fix this.
  ///
  /// TODO(b/139060984): After all filesystems are converted, revisit note.
  ///

    const Graph& graph = session->graph->graph;

    const string& mutation_warning = session->graph->sessions[session];
    if (!mutation_warning.empty()) {
      // TODO(b/74949947): turn this back into an error status
      LOG(WARNING) << mutation_warning;
      session->graph->sessions[session].clear();
    }

    const auto num_nodes = graph.num_node_ids();

        values
    *   `CategoryEncoding`: turn integer-encoded categories into one-hot,
        multi-hot, or tf-idf encoded representations
    *   `CategoryCrossing`: create new categorical features representing
        co-occurrences of previous categorical feature values
    *   `Hashing`: the hashing trick, for large-vocabulary categorical features
    *   `Discretization`: turn continuous numerical features into categorical