mutex_lock session_lock(it.first->mu);
    if (it.first->last_num_graph_nodes > op.node.id()) {
      it.second = strings::StrCat(
          "Operation '", op.node.DebugString(), "' was changed by ",
          mutation_type,
          " after it was run by a session. This mutation will have no effect, "
          "and will trigger an error in the future. Either don't modify "
          "nodes after running them or create a new session.");

  int num_retvals = 0;
  TFE_Execute(op, nullptr, &num_retvals, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteOp(op);

  // The device of var_handle1 is remote device, which was replaced during
  // cluster update. Removing resource with invalid device should fail
  // gracefully (i.e., with error status) instead of crashing with segfaults.
  op = TFE_NewOp(ctx, "DestroyResourceOp", status);

    TF_SetStatus(status, TF_INTERNAL, "OK status but no value was returned.");
    return;
  }

  std::vector<MaybeParallelTensorOwned> typed_outputs(
      std::move(maybe_typed_outputs.value()));

  if (typed_outputs.size() > *num_outputs) {
    TF_SetStatus(status, TF_INTERNAL,
                 "The allocated output buffer was too small.");
    return;
  }

  TFE_OpSetCancellationManager(op_.get(), wrap(cancellation_manager_), status);
  if (TF_GetCode(status) != TF_OK) return;

  // unwrap op_ and set step_id only if valid step id value was set.
  // Currently only required for non-TFRT use cases, e.g., EagerOp.
  if (step_id_.has_value()) {
    tensorflow::unwrap(op_.get())->SetStepId(step_id_.value());
  }

               second_negative_one.get(), status.get()));
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  // Un-pack the parallel tensor to verify that the operation was
  // successful. The resulting structure should be:
  //   second_device{first_device{1. * 3., 2. * 3.}, 3. * 3.}.
  std::array<TensorHandlePtr, 2> second_components;

                                  const std::shared_ptr<Block>& block,
                                  TF_Status* status) {
  absl::MutexLock lock(&mu_);
  if (block->timestamp == 0) {
    // The block was evicted from another thread. Allow it to remain evicted.
    return TF_SetStatus(status, TF_OK, "");
  }
  if (block->lru_iterator != lru_list_.begin()) {
    lru_list_.erase(block->lru_iterator);
    lru_list_.push_front(key);

  // LRU element was at offset 0.  Cache miss.
  calls.push_back(0);
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
  // Element at 2 * block_size is still in cache, and this read should update
  // its position in the LRU list so it doesn't get evicted by the next read.
  TF_EXPECT_OK(ReadCache(&cache, "", 2 * block_size, 1, &out));
  // Element at block_size was evicted.  Reading this element will also cause

  if (!forward_input) {
    TFE_DeleteTensorHandle(n);
  }
  TFE_DeleteOp(add_op);

  TF_Tensor* t = TFE_TensorHandleResolve(retval, status);
  if (async) {
    if (forward_input) {
      // Since the input was forwarded, we released the input handle right away
      // and hence expect the input to be forwarded to the return tensor.
      EXPECT_EQ(orig_ptr, TF_TensorData(t));
    } else {

    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TF_DeleteStatus(status);
  }

  // 6. Read `var` using `ctx_1`. This read should succeed since `ctx_1` was
  // created with `isolate_session_state` set to false, and update should
  // preserve it.
  {
    // Create a handle to `var`, using `ctx_1`.
    TFE_TensorHandle* var_handle =

  TF_Run(session, run_options, nullptr, nullptr, 0, nullptr, nullptr, 0,
         nullptr, 0, run_metadata, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ("Session was not created with a graph before Run()!",
            string(TF_Message(s)));
  TF_DeleteBuffer(run_metadata);
  TF_DeleteBuffer(run_options);

  TF_DeleteDeprecatedSession(session, s);