return OkStatus();
}

Status ModularWritableFile::Append(StringPiece data) {
  if (ops_->append == nullptr)
    return errors::Unimplemented(tensorflow::strings::StrCat(
        "Append() not implemented for ", filename_));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  ops_->append(file_.get(), data.data(), data.size(), plugin_status.get());
  return StatusFromTF_Status(plugin_status.get());
}

    forward_op_.inputs.push_back(input);
  }
  return OkStatus();
}
Status TapeOperation::SetAttrString(const char* attr_name, const char* data,
                                    size_t length) {
  forward_op_.attrs.Set(attr_name, StringPiece(data, length));
  return parent_op_->SetAttrString(attr_name, data, length);
}
Status TapeOperation::SetAttrInt(const char* attr_name, int64_t value) {

  std::vector<tensorflow::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {
  std::vector<TF_Output> return_tensors;
  std::vector<TF_Operation*> return_nodes;
  std::vector<const char*> missing_unused_key_names;
  std::vector<int> missing_unused_key_indexes;

  // Backing memory for missing_unused_key_names values.
  std::vector<tensorflow::string> missing_unused_key_names_data;
};

struct TF_DeviceList {

                 std::vector<char>* out) {
  out->clear();
  out->resize(n, 0);
  TF_Status status;
  auto bytes_transferred =
      cache->Read(filename, offset, n, out->data(), &status);
  if (bytes_transferred >= 0) {
    EXPECT_LE(bytes_transferred, n);
    out->resize(bytes_transferred, n);
  }
  return status.status;
}

TEST(RamFileBlockCacheTest, IsCacheEnabled) {

  bool did_work = false;
};

void SomeThreadFunc(void* data) {
  auto* real_data = static_cast<SomeThreadData*>(data);
  ::tensorflow::mutex_lock l(real_data->mu);
  real_data->did_work = true;
}

}  // namespace

TEST(TestEnv, TestThreads) {
  TF_ThreadOptions options;
  TF_DefaultThreadOptions(&options);
  SomeThreadData data;
  TF_Thread* thread =

  owner->reference.Unref();
  delete owner;
}

// Converts TF_DATAType to DLPack data type.
DLDataType GetDlDataType(TF_DataType data_type, TF_Status* status) {
  DLDataType dtype;
  dtype.lanes = 1;
  dtype.bits = TF_DataTypeSize(data_type) * 8;
  switch (data_type) {
    case TF_DataType::TF_BOOL:
      dtype.code = DLDataTypeCode::kDLBool;
      break;
    case TF_DataType::TF_HALF:

using tensorflow::NodeDef;

static void BoolDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<bool*>(data);
}

static void Int32Deallocator(void* data, size_t, void* arg) {
  delete[] static_cast<int32_t*>(data);
}

static void DoubleDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<double*>(data);
}

static void FloatDeallocator(void* data, size_t, void* arg) {
  delete[] static_cast<float*>(data);

  // shape is explicitly requested where possible.
  int (*num_dims)(void* data, TF_Status* status);

  // Computes the axis length at `dim_index`.
  int64_t (*dim)(void* data, int dim_index, TF_Status* status);

  void (*deallocator)(void* data);

  // Summarizes the value of this tensor. The caller takes ownership of the

 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will
 * hold the numerical gradient data at the end of the function.
 */
Status CalcNumericalGrad(AbstractContext* ctx, Model forward,
                         absl::Span<AbstractTensorHandle* const> inputs,
                         int input_index, bool use_function,

}

void TF_AppendWritableFile(TF_WritableFileHandle* handle, const char* data,
                           size_t length, TF_Status* status) {
  auto* cc_file = reinterpret_cast<::tensorflow::WritableFile*>(handle);
  TF_SetStatus(status, TF_OK, "");
  ::tensorflow::Set_TF_Status_from_Status(
      status, cc_file->Append(::tensorflow::StringPiece{data, length}));
}

void TF_DeleteFile(const char* filename, TF_Status* status) {