TracingOperationPtr operation(CreateOperation());
    TF_RETURN_IF_ERROR(operation->Reset("Placeholder", nullptr));
    TF_RETURN_IF_ERROR(
        operation->SetOpName(absl::StrCat("_input_", inputs_.size()).c_str()));
    TF_RETURN_IF_ERROR(operation->SetAttrType("dtype", dtype));
    if (!shape.unknown_rank()) {
      TF_RETURN_IF_ERROR(operation->SetAttrShape(

  PartialTensorShape shape;
  TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape));
  if (shape.dims() != 0) {
    return errors::InvalidArgument(
        "Tensor expected to have scalar shape found rank: ", shape.dims());
  }
  return absl::OkStatus();
}

TEST_P(UnifiedAPI, TestTensorShapeScalar) {
  if (UseFunction() && UseMlir()) {
    // TODO(b/173074167): Remove this.

    dimvec[i] = static_cast<int64_t>(dims[i]);
  }

  if (ctx == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid Context");
    return nullptr;
  }

  tensorflow::AbstractTensorInterface* t =
      tensorflow::unwrap(ctx)->CreateTensor(
          static_cast<tensorflow::DataType>(dtype), dimvec);

  if (t == nullptr) {
    status->status =

  tensorflow::AsyncValueTensor* av_tensor =
      tensorflow::AsyncValueTensor::FromTensor(tensor);
  if (av_tensor == nullptr || av_tensor->GetBuffer() == nullptr) {
    return absl::InternalError("Input tensor does not have PjRtBuffer.");
  }
  auto* c_api_buffer =
      dynamic_cast<xla::PjRtCApiBuffer*>(av_tensor->GetBuffer().get());
  if (c_api_buffer == nullptr) {
    return absl::InternalError(

namespace tensorflow {
namespace tracing {

Status MaybeSetOpName(AbstractOperation* op, const char* op_name) {
  if (isa<TracingOperation>(op)) {
    TF_RETURN_IF_ERROR(dyn_cast<TracingOperation>(op)->SetOpName(op_name));
  }
  if (isa<gradients::TapeOperation>(op)) {
    TF_RETURN_IF_ERROR(MaybeSetOpName(
        dyn_cast<gradients::TapeOperation>(op)->GetBackingOperation(),
        op_name));
  }

  } else {
    // Handles are on task0 (local), and task2, but op is on task1.
    matmul = MatMulOp(ctx, h0_task0, h1_task2);
  }
  if (remote) {
    TFE_OpSetDevice(matmul, task1_name, status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  } else if (!async) {
    // Set the local device to CPU to easily validate mirroring
    string cpu_device_name;

  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpSetAttrShape(op, "shape", {}, 0, status);
  TFE_OpSetAttrString(op, "container", "localhost", 0);
  TFE_OpSetAttrString(op, "shared_name", "", 0);
  if (!device_name.empty()) {
    TFE_OpSetDevice(op, device_name.c_str(), status);
  }
  if (TF_GetCode(status) != TF_OK) return nullptr;

  while (offset < size && rc > 0) {
    // Use uint64 for safe compare SSIZE_MAX
    uint64_t chunk = size - offset;
    if (chunk > SSIZE_MAX) chunk = SSIZE_MAX;

    rc = sendfile(dst_fd, src_fd, &offset, static_cast<size_t>(chunk));
    if (rc < 0) return -1;
    bytes_transferred += rc;
  }

  return bytes_transferred;
}

  TF_DeleteTensor(tensor_1X1X6);
  TF_DeleteTensor(tensor_1X6);
}

TEST(CAPI_EXPERIMENTAL, LibraryPluggableDeviceLoadFunctions) {
  // TODO(penpornk): Enable this test on Windows.
#if !defined(PLATFORM_WINDOWS)
#if !defined(TENSORFLOW_NO_SHARED_OBJECTS)
  // Load the library.
  TF_Status* status = TF_NewStatus();
  string lib_path =
      tensorflow::GetDataDependencyFilepath(tensorflow::io::JoinPath(

      ASSERT_TRUE(a_edges.find(e) != a_edges.end())
          << "Failed to find expected control edge " << e.ToString()
          << " in fdef: " << fdef.DebugString();
    }

    // If caller specified all edges, check that we have seen all
    if (is_exact_edges) {
      ASSERT_EQ(e_edges.size() + c_edges.size(), a_edges.size())
          << "Expected edges: " << ToString(e_edges)
          << " Expected Control edges: " << ToString(c_edges)