if (num_dims == kUnknownRank) {
      return absl::OkStatus();
    }

    std::vector<int64_t> dims(num_dims, kUnknownDim);
    TF_GraphGetTensorShape(graph_, output_,
                           reinterpret_cast<int64_t*>(dims.data()), num_dims,
                           &status);
    TF_RETURN_IF_ERROR(StatusFromTF_Status(&status));
    TF_RETURN_IF_ERROR(tensorflow::TensorShapeUtils::MakeShape(dims, shape));

  }
  tensorflow::PartialTensorShape partial_shape;
  if (shape.num_dims != -1) {
    DCHECK(shape.dim_sizes != nullptr);
    Status status = tensorflow::PartialTensorShape::MakePartialShape(
        reinterpret_cast<int64_t*>(shape.dim_sizes), shape.num_dims,
        &partial_shape);
    if (!status.ok()) {
      tsl::Set_TF_Status_from_Status(s, status);
      return nullptr;
    }
  }

        methods_.summarize(data_, &c_status), TF_DeleteBuffer);
    if (!c_status.status.ok()) {
      return c_status.status;
    }
    summary = std::string(reinterpret_cast<const char*>(summary_buffer->data),
                          summary_buffer->length);
    return absl::OkStatus();
  }

 private:
  void* const data_;

// may lead to collisions. Introduce another way to get a unique id for this
// tensor.
int64_t ToId(const AbstractTensorHandle* t) {
  return static_cast<int64_t>(reinterpret_cast<uintptr_t>(t));
}

Status ZerosLike(AbstractContext* ctx, AbstractTensorHandle* t,
                 AbstractTensorHandle** result) {
  AbstractOperationPtr op(ctx->CreateOperation());

  TF_DeleteStatus(s);
}

void Deallocator(void* data, size_t, void* arg) {
  tensorflow::cpu_allocator()->DeallocateRaw(data);
  *reinterpret_cast<bool*>(arg) = true;
}

TEST(CAPI, Tensor) {
  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(
          TF_TensorDefaultAlignment(), num_bytes));
  int64_t dims[] = {2, 3};

  for (int i = 0; i < shape.dims(); ++i) {
    dims.push_back(shape.dim_size(i));
    nelems *= shape.dim_size(i);
  }
  CHECK_EQ(nelems, 0);
  return TF_NewTensor(
      dtype, reinterpret_cast<const int64_t*>(dims.data()), shape.dims(),
      reinterpret_cast<void*>(&empty), 0, [](void*, size_t, void*) {}, nullptr);
}

static void TF_Run_Helper(
    Session* session, const char* handle, const TF_Buffer* run_options,

  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, tensorflow::gtl::ArraySlice<const tensorflow::DataType>(
                          reinterpret_cast<const tensorflow::DataType*>(values),
                          num_values));
}

void TF_AttrBuilderCheckCanRunOnDevice(TF_AttrBuilder* builder,
                                       const char* device_type,

message EndpointConditions {
  // ready indicates that this endpoint is prepared to receive traffic,
  // according to whatever system is managing the endpoint. A nil value
  // indicates an unknown state. In most cases consumers should interpret this
  // unknown state as ready. For compatibility reasons, ready should never be
  // "true" for terminating endpoints, except when the normal readiness
  // behavior is being explicitly overridden, for example when the associated

  // Annotations is a list of key-value pairs extracted from the Pod's annotations.
  // It only includes keys which match the pattern `*.image-policy.k8s.io/*`.
  // It is up to each webhook backend to determine how to interpret these annotations, if at all.
  // +optional
  map<string, string> annotations = 2;

  // Namespace is the namespace the pod is being created in.
  // +optional
  optional string namespace = 3;
}

message EndpointConditions {
  // ready indicates that this endpoint is prepared to receive traffic,
  // according to whatever system is managing the endpoint. A nil value
  // indicates an unknown state. In most cases consumers should interpret this
  // unknown state as ready. For compatibility reasons, ready should never be
  // "true" for terminating endpoints.
  // +optional
  optional bool ready = 1;