}
    }
  });
  // 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) {
      case FetchState::ERROR:
        // TF_FALLTHROUGH_INTENDED
      case FetchState::CREATED:
        block->state = FetchState::FETCHING;

  EXPECT_EQ(out.size(), 1);
  // Now read the first block; this should yield an INTERNAL error because we
  // had already cached a partial block at a later position.
  Status status = ReadCache(&cache, "", 0, block_size, &out);
  EXPECT_EQ(status.code(), error::INTERNAL);
}

TEST(RamFileBlockCacheTest, LRU) {
  const size_t block_size = 16;
  std::list<size_t> calls;

  // Step 1: Load plugin
  Env* env = Env::Default();
  void* dso_handle;
  TF_RETURN_IF_ERROR(env->LoadDynamicLibrary(dso_path.c_str(), &dso_handle));

  // Step 2: Load symbol for `TF_InitPlugin`
  void* dso_symbol;
  TF_RETURN_WITH_CONTEXT_IF_ERROR(
      env->GetSymbolFromLibrary(dso_handle, "TF_InitPlugin", &dso_symbol),
      "Failed to load TF_InitPlugin symbol for DSO: ", dso_path);

#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/framework/full_type.pb.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/test.h"

using tensorflow::Status;
using tensorflow::string;
using tensorflow::TF_StatusPtr;

namespace tensorflow {

  }
  registry_.insert({op_name, gradient_function_factory});
  return absl::OkStatus();
}
Status GradientRegistry::Lookup(
    const ForwardOperation& op,
    std::unique_ptr<GradientFunction>* gradient_function) const {
  auto iter = registry_.find(op.op_name);
  if (iter == registry_.end()) {
    const string error_msg = "No gradient defined for op: " + op.op_name + ".";
    return errors::NotFound(error_msg);

  OwnedTFStatus c_status(TF_NewStatus());
  init_fn(&params, c_status.get());
  TF_RETURN_IF_ERROR(tsl::StatusFromTF_Status(c_status.get()));
  TF_RETURN_IF_ERROR(ValidateTPOptimizerRegistrationParams(params));
  TF_RETURN_IF_ERROR(ValidateTPOptimizer(optimizer));
  TF_RETURN_IF_ERROR(ValidateTPOptimizerConfigs(optimizer_configs));

  CGraphOptimizerRegister(

                                                    TF_Status* status);

// Report error (specified by error_code and error_message) to other tasks in
// the cluster.
TF_CAPI_EXPORT extern void TFE_ReportErrorToCluster(TFE_Context* ctx,
                                                    int error_code,
                                                    const char* error_message,
                                                    TF_Status* status);

    // Get transpose attrs
    bool t_a;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_a", &t_a));

    bool t_b;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_b", &t_b));

    // Conj each input
    AbstractTensorHandle* conj_output;
    std::string name = "Conj_A_MatMul_Grad";
    TF_RETURN_IF_ERROR(
        SafeConj(ctx, forward_inputs_[0], &conj_output, name.c_str()));

// type of files.
static Status ValidateOperations(const TF_FilesystemPluginOps* ops) {
  TF_RETURN_IF_ERROR(ValidateHelper(ops->filesystem_ops));
  TF_RETURN_IF_ERROR(ValidateHelper(ops->random_access_file_ops));
  TF_RETURN_IF_ERROR(ValidateHelper(ops->writable_file_ops));
  TF_RETURN_IF_ERROR(ValidateHelper(ops->read_only_memory_region_ops));

  if (ops->filesystem_ops->new_random_access_file != nullptr &&

    TracingOperationPtr operation(CreateOperation());
    TF_RETURN_IF_ERROR(operation->Reset("Placeholder", nullptr));
    TF_RETURN_IF_ERROR(
        operation->SetOpName(absl::StrCat("_input_", inputs_.size()).c_str()));
    TF_RETURN_IF_ERROR(operation->SetAttrType("dtype", dtype));
    if (!shape.unknown_rank()) {
      TF_RETURN_IF_ERROR(operation->SetAttrShape(