==============================================================================*/

#include "tensorflow/c/c_api.h"

#include <algorithm>
#include <cstring>
#include <limits>
#include <memory>
#include <optional>
#include <unordered_set>
#include <utility>
#include <vector>

#include "absl/strings/match.h"
// Required for IS_MOBILE_PLATFORM
#include "tensorflow/core/platform/platform.h"  // NOLINT

#endif
  } else {
    status->status = tensorflow::errors::InvalidArgument(
        "Unsupported Device Type for dlpack");
  }

  return ctx;
}

// Converts DLDevice to TF device name.
absl::optional<std::string> DeviceNameFromDlContext(const DLDevice& ctx,
                                                    TF_Status* status) {
  switch (ctx.device_type) {
    case DLDeviceType::kDLCPU:
      return "CPU:0";

                 TF_Message(first_bad_status.get()));
  }
}

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>
ParallelDevice::Join(
    const std::vector<PartialTensorShape>& expected_output_shapes,
    TF_Status* status) const {
  absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> result;
  // Compute per-device per-output tensors

};

absl::optional<std::vector<MaybeParallelTensorOwned>> ExecuteWithSpecialOps(
    const ParallelDevice& parallel_device,
    const std::string& parallel_device_name, TFE_Context* context,
    std::vector<MaybeParallelTensorUnowned> inputs, const char* operation_name,
    const TFE_OpAttrs* attributes, int expected_max_outputs,
    TF_Status* status) {
  absl::optional<std::vector<MaybeParallelTensorOwned>> result;

==============================================================================*/
#include "tensorflow/c/experimental/next_pluggable_device/tensor_pjrt_buffer_util.h"

#include <cstdint>
#include <memory>
#include <optional>
#include <utility>
#include <vector>

#include <gtest/gtest.h>
#include "absl/log/check.h"
#include "xla/pjrt/c/pjrt_c_api.h"
#include "xla/pjrt/c/pjrt_c_api_cpu.h"

// If the flag is not used, no shared objects are loaded.
//
// Every filesystem provides support for accessing URIs of form
// `[<scheme>://]<path>` where `<scheme>` is optional (if missing, we are
// accessing local paths). This test suite tests exactly one scheme for each
// invocation. By default, we are testing all schemes available but this can be

    }
    TF_DeleteShapeAndTypeList(input_shapes);
    TF_DeleteShapeAndTypeList(output_shapes);
  }

  absl::optional<std::vector<int64_t>> make_shape(
      std::vector<int64_t>&& dims) const {
    return absl::make_optional(dims);
  }

  absl::optional<std::vector<int64_t>> unknown_shape() const {
    return absl::nullopt;
  }

  static constexpr int64_t kUnknownDim =

}

// Validates the read only memory region operations given by the plugin.
static Status ValidateHelper(const TF_ReadOnlyMemoryRegionOps* ops) {
  if (ops == nullptr) {
    // read only memory region support is always optional
    return OkStatus();
  }

  if (ops->cleanup == nullptr)
    return errors::FailedPrecondition(
        "Trying to register filesystem without `cleanup` operation on read "
        "only memory regions");

void TFE_RegisterCustomDevice(TFE_Context* ctx, TFE_CustomDevice device,
                              const char* device_name, void* device_info,
                              TF_Status* status) {
  // Fill in default values for optional functionality.
  if (device.pack == nullptr) {
    device.pack = &DefaultCustomDevicePack;
  }
  auto custom_device = std::make_unique<tensorflow::CustomDeviceAPI>(
      ctx, device, device_info, device_name);