TFE_DeleteContextOptions(opts);
  TFE_ContextAddFunction(ctx, fn, status);
  ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
  TF_DeleteFunction(fn);

  for (bool async : {false, true, false}) {
    TFE_Executor* old_executor = TFE_ContextGetExecutorForThread(ctx);
    TFE_Executor* executor = TFE_NewExecutor(
        /*is_async=*/async, /*enable_streaming_enqueue=*/true,

  tf_gcs_filesystem::RamFileBlockCache cache3(0, 32, 0, fetcher);
  tf_gcs_filesystem::RamFileBlockCache cache4(16, 32, 0, fetcher);

  EXPECT_FALSE(cache1.IsCacheEnabled());
  EXPECT_FALSE(cache2.IsCacheEnabled());
  EXPECT_FALSE(cache3.IsCacheEnabled());
  EXPECT_TRUE(cache4.IsCacheEnabled());
}

TEST(RamFileBlockCacheTest, ValidateAndUpdateFileSignature) {
  int calls = 0;

}

void RamFileBlockCache::MaybeFetch(const Key& key,
                                   const std::shared_ptr<Block>& block,
                                   TF_Status* status) {
  bool downloaded_block = false;
  auto reconcile_state = MakeCleanup([this, &downloaded_block, &key, &block] {
    // Perform this action in a cleanup callback to avoid locking mu_ after
    // locking block->mu.
    if (downloaded_block) {

                         ::testing::Combine(::testing::Values("graphdef",
                                                              "mlir"),
                                            ::testing::Values(false)));

}  // namespace

}

@test "Pip package doesn't depend on CUDA for static builds (i.e. Windows)" {
  bazel cquery \
    --experimental_cc_shared_library \
    --@local_config_cuda//:enable_cuda \
    --define framework_shared_object=false \
    "somepath(//tensorflow/tools/pip_package:build_pip_package, " \
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cuda_driver + "\

  num_retvals = 1;
  executed = false;
  TFE_Execute(matmul.get(), &retval, &num_retvals, status.get());
  EXPECT_FALSE(executed);
  ASSERT_FALSE(TF_GetCode(status.get()) == TF_OK);

  // Custom devices can refuse to do type-based dispatch (as hcustom1 is
  // configured to do)
  matmul.reset(MatMulOp(context.get(), hcustom1.get(), hcpu.get()));
  num_retvals = 1;
  executed = false;

  if (node_def.op() != "Placeholder" || node_def.name() != "feed") {
    return false;
  }
  bool found_dtype = false;
  bool found_shape = false;
  for (const auto& attr : node_def.attr()) {
    if (attr.first == "dtype") {
      if (attr.second.type() == tensorflow::DT_INT32) {
        found_dtype = true;
      } else {
        return false;
      }
    } else if (attr.first == "shape") {
      found_shape = true;

    if (shape_arr[i] == 0) return true;
    // Note that dimensions with size=1 can have any stride.
    if (shape_arr[i] != 1 && stride_arr[i] != expected_stride) {
      valid = false;
    }
    expected_stride *= shape_arr[i];
  }
  return valid;
}
}  // namespace

void TFE_CallDLManagedTensorDeleter(void* dlm_ptr) {

// referenced by the inflight nodes.
// A recommended value has not been established.
// A value of 0 removes the limit, which is the behavior of TensorFlow 2.11.
// When is_async is false, the value is ignored.
TF_CAPI_EXPORT extern TFE_Executor* TFE_NewExecutor(
    bool is_async, bool enable_streaming_enqueue, int in_flight_nodes_limit);

    }
  }

  TF_RETURN_IF_ERROR(GradientTape::ComputeGradient(
      vspace, target_tensor_ids, source_tensor_ids, sources_that_are_targets,
      output_gradients, result, /*build_default_zeros_grads*/ false));
  return absl::OkStatus();
}

// Helper functions which delegate to `AbstractOperation`, update
// the state of the ForwardOperation and call the tape as appropriate.