lra_list_.erase(block->lra_iterator);
        lra_list_.push_front(key);
        block->lra_iterator = lra_list_.begin();
        block->timestamp = timer_seconds_();
      }
    }
  });
  // Loop until either block content is successfully fetched, or our request
  // encounters an error.
  absl::MutexLock l(&block->mu);
  TF_SetStatus(status, TF_OK, "");
  while (true) {
    switch (block->state) {

      TF_GUARDED_BY(mu);
  bool delete_requested TF_GUARDED_BY(mu);  // set true by TF_DeleteGraph

  // Used to link graphs contained in TF_WhileParams to the parent graph that
  // will eventually contain the full while loop.
  TF_Graph* parent;
  TF_Output* parent_inputs;
};

struct TF_OperationDescription {
  TF_OperationDescription(TF_Graph* g, const char* op_type,
                          const char* node_name)

      block_size, block_count * block_size, 0, fetcher);
  std::vector<char> out;
  out.resize(block_count, 0);
  // The cache has space for `block_count` blocks. The loop with i = 0 should
  // fill the cache, and the loop with i = 1 should be all cache hits. The
  // fetcher checks that it is called once and only once for each offset (to
  // fetch the corresponding block).
  for (int i = 0; i < 2; i++) {

  TF_Output cond_output;

  // The loop body graph. The inputs are the current values of the loop
  // variables. The outputs are the updated values of the loop variables.
  TF_Graph* const body_graph;
  const TF_Output* const body_inputs;
  TF_Output* const body_outputs;

  // Unique null-terminated name for this while loop. This is used as a prefix
  // for created operations.
  const char* name;
} TF_WhileParams;

## Known Caveats

*   TF Core:
    *   A longstanding bug in `tf.while_loop`, which caused it to execute
        sequentially, even when `parallel_iterations>1`, has now been fixed.
        However, the increased parallelism may result in increased memory use.
        Users who experience unwanted regressions should reset their
        `while_loop`'s `parallel_iterations` value to 1, which is consistent
        with prior behavior.

        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
    ],
)

tf_cc_test(
    name = "while_loop_test",
    size = "medium",
    srcs = ["while_loop_test.cc"],
    deps = [
        ":c_api",
        ":c_test_util",
        "//tensorflow/core:lib",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",

  TF_TriState auto_mixed_precision_onednn_bfloat16;
  TF_TriState auto_mixed_precision_mkl;
  TF_TriState pin_to_host_optimization;
  TF_TriState layout_optimizer;
  TF_TriState remapping;
  TF_TriState loop_optimization;
  TF_TriState dependency_optimization;
  TF_TriState auto_parallel;
  TF_TriState memory_optimization;
  TF_TriState scoped_allocator_optimization;
} TP_OptimizerConfigs;

    if (new_node == nullptr) continue;
    parent->name_map[new_node->name()] = new_node;
  }

  // Populate 'outputs'.
  DCHECK_LE(loop_outputs.size(), num_loop_vars);
  for (int i = 0; i < loop_outputs.size(); ++i) {
    outputs[i] = {ToOperation(loop_outputs[i].node()), loop_outputs[i].index()};
  }
}

}  // namespace
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)

  CONFIG_TOGGLE(auto_mixed_precision_onednn_bfloat16);
  CONFIG_TOGGLE(auto_mixed_precision_mkl);
  CONFIG_TOGGLE(pin_to_host_optimization);
  CONFIG_TOGGLE(layout_optimizer);
  CONFIG_TOGGLE(remapping);
  CONFIG_TOGGLE(loop_optimization);
  CONFIG_TOGGLE(dependency_optimization);
  CONFIG_TOGGLE(auto_parallel);
  CONFIG_TOGGLE(memory_optimization);
  CONFIG_TOGGLE(scoped_allocator_optimization);

  TensorHandlePtr combined_value = CreatePerDeviceValues(
      context.get(), in_components, device_name, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  // Loop to make synchronization failures more deterministic
  for (int i = 0; i < 100; ++i) {
    TensorHandlePtr multiply_result(
        Multiply(context.get(), combined_value.get(), combined_value.get(),