}

  // Populate return_nodes
  DCHECK(tf_results->return_nodes.empty());
  tf_results->return_nodes.resize(results.return_nodes.size());
  for (int i = 0; i < results.return_nodes.size(); ++i) {
    tf_results->return_nodes[i] = ToOperation(results.return_nodes[i]);
  }

  // Populate missing unused map keys
  DCHECK(tf_results->missing_unused_key_names.empty());
  DCHECK(tf_results->missing_unused_key_indexes.empty());

  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

  TFE_TensorHandle* h = TestScalarTensorHandle(ctx, 1.0f);
  TFE_TensorDebugInfo* debug_info = TFE_TensorHandleTensorDebugInfo(h, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

}

TF_AbstractTensor* TF_AddFunctionParameter(TF_ExecutionContext* func,
                                           TF_DataType dtype, TF_Shape shape,
                                           TF_Status* s) {
  DCHECK_GE(shape.num_dims, -1);
  TracingTensorHandle* t;
  TracingContext* tracing_ctx = dyn_cast<TracingContext>(unwrap(func));
  if (!tracing_ctx) {
    tsl::Set_TF_Status_from_Status(

     *
     *    dA = U
     *    dbias = reduceSum(U, dims = channel_dim)
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];
    DCHECK(upstream_grad);

    // Recover data format from forward pass for gradient.
    std::string data_format;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("data_format", &data_format));

    // Grad for A

                                 const int64_t* dims, int num_dims) {
  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape = shape_list->items[index];
  DCHECK(shape.dims == nullptr) << "Shape at " << index << " is already set!";
  DCHECK(num_dims >= 0) << "Number of dimensions cannot be negative!";
  shape.num_dims = num_dims;
  shape.dims = new int64_t[num_dims];

    CHECK_EQ(errors::OK, s.code()) << s.message();
  }
};

class PassThroughGradientFunction : public GradientFunction {
 public:
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    CHECK_EQ(grad_outputs.size(), 1);
    CHECK_EQ(grad_inputs.size(), 1);

                          TFE_OpGetAttrs(read_op.get()),
                          /*expected_max_outputs=*/1, status.get());
  ASSERT_FALSE(TF_GetCode(status.get()) == TF_OK);
  TF_SetStatus(status.get(), TF_OK, "");

  // Check that ops still run successfully on the device.
  parallel_device.Execute(context.get(), std::vector<ParallelTensor*>(),
                          "VarHandleOp", TFE_OpGetAttrs(handle_op.get()),

  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Create a new tensor_handle.
  TFE_TensorHandle* h0_task0_new = TestMatrixTensorHandle(ctx);

  // Check that copying it to the old remote device (named localhost) fails.
  TFE_TensorHandleCopyToDevice(h0_task0_new, ctx, remote_device_name, status);
  EXPECT_NE(TF_OK, TF_GetCode(status)) << TF_Message(status);

    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_Execute(op, &h, &num_retvals, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    CHECK_EQ(1, num_retvals);
    CHECK(h);
    CHECK_EQ(TF_FLOAT, TFE_TensorHandleDataType(h));
    CHECK_EQ(0, TFE_TensorHandleNumDims(h, status));
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    h = nullptr;
    TFE_DeleteOp(op);
  }

CheckpointReader::BuildV2VarMaps() {
  CHECK(v2_reader_ != nullptr);
  CHECK(v2_reader_->status().ok());

  // First pass: filters out the entries of the slices.
  std::unordered_set<string> filtered_keys;
  BundleEntryProto entry;
  v2_reader_->Seek(kHeaderEntryKey);
  for (v2_reader_->Next(); v2_reader_->Valid(); v2_reader_->Next()) {
    CHECK(entry.ParseFromArray(v2_reader_->value().data(),