void SaveTensors(
    OpKernelContext* context,
    checkpoint::TensorSliceWriter::CreateBuilderFunction builder_func,
    bool save_slices) {
  const Tensor& filename_t = context->input(0);
  {
    const int64_t size = filename_t.NumElements();
    OP_REQUIRES(
        context, size == 1,
        errors::InvalidArgument(
            "Input 0 (filename) must be a string scalar; got a tensor of ",
            size, "elements"));
  }

  // Path, names, and slices if save_slices is true.
  const int kFixedInputs = save_slices ? 3 : 2;
  const Tensor& tensor_names_t = context->input(1);
  OP_REQUIRES(context,
              FastBoundsCheck(tensor_names_t.NumElements() + kFixedInputs,
                              std::numeric_limits<int>::max()),
              errors::InvalidArgument("Too many inputs to SaveTensors"));
  const int N = static_cast<int>(tensor_names_t.NumElements());
  const tstring* tensor_shapes_and_slices_ptr = nullptr;
  if (save_slices) {
    const Tensor& tensor_shapes_and_slices_t = context->input(2);
    OP_REQUIRES(
        context,
        tensor_shapes_and_slices_t.NumElements() == static_cast<int64>(N),
        errors::InvalidArgument("Expected ", N,
                                " elements for the tensor "
                                "shapes and slices but got ",
                                tensor_shapes_and_slices_t.NumElements()));
    tensor_shapes_and_slices_ptr =
        tensor_shapes_and_slices_t.flat<tstring>().data();
  }
  OP_REQUIRES(context, context->num_inputs() == N + kFixedInputs,
              errors::InvalidArgument("Expected totally ", N + kFixedInputs,
                                      " inputs as input #1 (which is a string "
                                      "tensor of saved names) contains ",
                                      N, " names, but received ",
                                      context->num_inputs(), " inputs"));

  VLOG(1) << "About to save tensors to file " << filename_t.flat<tstring>()(0)
          << "...";
  checkpoint::TensorSliceWriter writer(filename_t.flat<tstring>()(0),
                                       std::move(builder_func));

  Status s;
  auto tensor_names_flat = tensor_names_t.flat<tstring>();

  // Process tensors in sorted name order.  This allows us to avoid seeking
  // during restoration in the common case where we are restoring a full
  // checkpoint.
  std::vector<size_t> sorted_name_idx(tensor_names_flat.size());
  std::iota(sorted_name_idx.begin(), sorted_name_idx.end(), 0);
  std::sort(sorted_name_idx.begin(), sorted_name_idx.end(),
            [&tensor_names_flat](size_t a, size_t b) {
              return tensor_names_flat(a) < tensor_names_flat(b);
            });

  for (const size_t i : sorted_name_idx) {
    const string& name = tensor_names_flat(i);
    const Tensor& input = context->input(i + kFixedInputs);
    TensorShape shape(input.shape());
    TensorSlice slice(input.dims());
    if (save_slices && !tensor_shapes_and_slices_ptr[i].empty()) {
      const tstring& shape_spec = tensor_shapes_and_slices_ptr[i];
      TensorShape slice_shape;
      OP_REQUIRES_OK(context, checkpoint::ParseShapeAndSlice(
                                  shape_spec, &shape, &slice, &slice_shape));
      OP_REQUIRES(context, slice_shape.IsSameSize(input.shape()),
                  errors::InvalidArgument(
                      "Slice in shape_and_slice "
                      "specification does not match the "
                      "shape of the tensor to  save: ",
                      shape_spec, ", tensor: ", input.shape().DebugString()));
    }

#define WRITER_ADD(T)                                           \
  case DataTypeToEnum<T>::value:                                \
    s = writer.Add(name, shape, slice, input.flat<T>().data()); \
    break;

    switch (input.dtype()) {
      TF_CALL_SAVE_RESTORE_TYPES(WRITER_ADD)
      default:
        context->SetStatus(errors::Unimplemented("Saving data type ",
                                                 DataTypeString(input.dtype()),
                                                 " not yet supported"));
        return;
    }
#undef WRITER_ADD
    if (!s.ok()) {
      context->SetStatus(s);
      return;
    }
  }

  s = writer.Finish();
  if (!s.ok()) {
    context->SetStatus(s);
  }
}
void SaveTensors(
    OpKernelContext* context,
    checkpoint::TensorSliceWriter::CreateBuilderFunction builder_func,
    bool save_slices) {
  const Tensor& filename_t = context->input(0);
  {
    const int64_t size = filename_t.NumElements();
    OP_REQUIRES(
        context, size == 1,
        errors::InvalidArgument(
            "Input 0 (filename) must be a string scalar; got a tensor of ",
            size, "elements"));
  }

   
  const int kFixedInputs = save_slices ? 3 : 2;
  const Tensor& tensor_names_t = context->input(1);
  OP_REQUIRES(context,
              FastBoundsCheck(tensor_names_t.NumElements() + kFixedInputs,
                              std::numeric_limits<int>::max()),
              errors::InvalidArgument("Too many inputs to SaveTensors"));
  const int N = static_cast<int>(tensor_names_t.NumElements());
  const tstring* tensor_shapes_and_slices_ptr = nullptr;
  if (save_slices) {
    const Tensor& tensor_shapes_and_slices_t = context->input(2);
    OP_REQUIRES(
        context,
        tensor_shapes_and_slices_t.NumElements() == static_cast<int64>(N),
        errors::InvalidArgument("Expected ", N,
                                " elements for the tensor "
                                "shapes and slices but got ",
                                tensor_shapes_and_slices_t.NumElements()));
    tensor_shapes_and_slices_ptr =
        tensor_shapes_and_slices_t.flat<tstring>().data();
  }
  OP_REQUIRES(context, context->num_inputs() == N + kFixedInputs,
              errors::InvalidArgument("Expected totally ", N + kFixedInputs,
                                      " inputs as input #1 (which is a string "
                                      "tensor of saved names) contains ",
                                      N, " names, but received ",
                                      context->num_inputs(), " inputs"));

  VLOG(1) << "About to save tensors to file " << filename_t.flat<tstring>()(0)
          << "...";
  checkpoint::TensorSliceWriter writer(filename_t.flat<tstring>()(0),
                                       std::move(builder_func));

  Status s;
  auto tensor_names_flat = tensor_names_t.flat<tstring>();

   
   
   
  std::vector<size_t> sorted_name_idx(tensor_names_flat.size());
  std::iota(sorted_name_idx.begin(), sorted_name_idx.end(), 0);
  std::sort(sorted_name_idx.begin(), sorted_name_idx.end(),
            [&tensor_names_flat](size_t a, size_t b) {
              return tensor_names_flat(a) < tensor_names_flat(b);
            });

  for (const size_t i : sorted_name_idx) {
    const string& name = tensor_names_flat(i);
    const Tensor& input = context->input(i + kFixedInputs);
    TensorShape shape(input.shape());
    TensorSlice slice(input.dims());
    if (save_slices && !tensor_shapes_and_slices_ptr[i].empty()) {
      const tstring& shape_spec = tensor_shapes_and_slices_ptr[i];
      TensorShape slice_shape;
      OP_REQUIRES_OK(context, checkpoint::ParseShapeAndSlice(
                                  shape_spec, &shape, &slice, &slice_shape));
      OP_REQUIRES(context, slice_shape.IsSameSize(input.shape()),
                  errors::InvalidArgument(
                      "Slice in shape_and_slice "
                      "specification does not match the "
                      "shape of the tensor to  save: ",
                      shape_spec, ", tensor: ", input.shape().DebugString()));
    }

#define WRITER_ADD(T)                                           \
  case DataTypeToEnum<T>::value:                                \
    s = writer.Add(name, shape, slice, input.flat<T>().data()); \
    break;

    switch (input.dtype()) {
      TF_CALL_SAVE_RESTORE_TYPES(WRITER_ADD)
      default:
        context->SetStatus(errors::Unimplemented("Saving data type ",
                                                 DataTypeString(input.dtype()),
                                                 " not yet supported"));
        return;
    }
#undef WRITER_ADD
    if (!s.ok()) {
      context->SetStatus(s);
      return;
    }
  }

  s = writer.Finish();
  if (!s.ok()) {
    context->SetStatus(s);
  }
}