  void ComputeAsync(OpKernelContext* context, DoneCallback done) override {
    // The shape of 'grads' is [num_boxes, crop_height, crop_width, depth].
    const Tensor& grads = context->input(0);
    // The shape of 'boxes' is [num_boxes, 4].
    const Tensor& boxes = context->input(1);
    // The shape of 'box_index' is [num_boxes].
    const Tensor& box_index = context->input(2);
    // The shape of 'image_size' is [4].
    const Tensor& image_size = context->input(3);

    // Validate input shapes.
    OP_REQUIRES_ASYNC(context, grads.dims() == 4,
                      errors::InvalidArgument("grads image must be 4-D",
                                              grads.shape().DebugString()),
                      done);
    const int crop_height = grads.dim_size(1);
    const int crop_width = grads.dim_size(2);
    OP_REQUIRES_ASYNC(
        context, crop_height > 0 && crop_width > 0,
        errors::InvalidArgument("grads dimensions must be positive"), done);
    int num_boxes = 0;
    OP_REQUIRES_OK_ASYNC(
        context, ParseAndCheckBoxSizes(boxes, box_index, &num_boxes), done);
    OP_REQUIRES_ASYNC(
        context, grads.dim_size(0) == num_boxes,
        errors::InvalidArgument("boxes and grads have incompatible shape"),
        done);

    OP_REQUIRES_ASYNC(context, image_size.dims() == 1,
                      errors::InvalidArgument("image_size must be 1-D",
                                              image_size.shape().DebugString()),
                      done);
    OP_REQUIRES_ASYNC(context, image_size.dim_size(0) == 4,
                      errors::InvalidArgument("image_size must have 4 elements",
                                              image_size.shape().DebugString()),
                      done);
    auto image_size_vec = image_size.vec<int32>();
    const int batch_size = internal::SubtleMustCopy(image_size_vec(0));
    const int image_height = internal::SubtleMustCopy(image_size_vec(1));
    const int image_width = internal::SubtleMustCopy(image_size_vec(2));
    const int depth = internal::SubtleMustCopy(image_size_vec(3));
    OP_REQUIRES_ASYNC(
        context, image_height > 0 && image_width > 0,
        errors::InvalidArgument("image dimensions must be positive"), done);
    OP_REQUIRES_ASYNC(
        context, grads.dim_size(3) == depth,
        errors::InvalidArgument("image_size and grads are incompatible"), done);

    // Allocate output tensor.
    Tensor* output = nullptr;
    OP_REQUIRES_OK_ASYNC(
        context,
        context->allocate_output(
            0, TensorShape({batch_size, image_height, image_width, depth}),
            &output),
        done);

    auto compute_callback = [this, context, output]() {
      const Tensor& grads = context->input(0);
      const Tensor& boxes = context->input(1);
      const Tensor& box_index = context->input(2);
      const bool status = functor::CropAndResizeBackpropImage<Device, T>()(
          context, grads.tensor<float, 4>(), boxes.tensor<float, 2>(),
          box_index.tensor<int32, 1>(), output->tensor<T, 4>(), method_);

      if (!status) {
        context->SetStatus(errors::Internal(
            "Failed launch CropAndResizeBackpropImage kernel."));
      }
    };

    RunIfBoxIndexIsValid<Device>(context, box_index.tensor<int32, 1>(),
                                 batch_size, std::move(compute_callback),
                                 std::move(done));
  }
  void ComputeAsync(OpKernelContext* context, DoneCallback done) override {
     
    const Tensor& grads = context->input(0);
     
    const Tensor& boxes = context->input(1);
     
    const Tensor& box_index = context->input(2);
     
    const Tensor& image_size = context->input(3);

     
    OP_REQUIRES_ASYNC(context, grads.dims() == 4,
                      errors::InvalidArgument("grads image must be 4-D",
                                              grads.shape().DebugString()),
                      done);
    const int crop_height = grads.dim_size(1);
    const int crop_width = grads.dim_size(2);
    OP_REQUIRES_ASYNC(
        context, crop_height > 0 && crop_width > 0,
        errors::InvalidArgument("grads dimensions must be positive"), done);
    int num_boxes = 0;
    OP_REQUIRES_OK_ASYNC(
        context, ParseAndCheckBoxSizes(boxes, box_index, &num_boxes), done);
    OP_REQUIRES_ASYNC(
        context, grads.dim_size(0) == num_boxes,
        errors::InvalidArgument("boxes and grads have incompatible shape"),
        done);

    OP_REQUIRES_ASYNC(context, image_size.dims() == 1,
                      errors::InvalidArgument("image_size must be 1-D",
                                              image_size.shape().DebugString()),
                      done);
    OP_REQUIRES_ASYNC(context, image_size.dim_size(0) == 4,
                      errors::InvalidArgument("image_size must have 4 elements",
                                              image_size.shape().DebugString()),
                      done);
    auto image_size_vec = image_size.vec<int32>();
    const int batch_size = internal::SubtleMustCopy(image_size_vec(0));
    const int image_height = internal::SubtleMustCopy(image_size_vec(1));
    const int image_width = internal::SubtleMustCopy(image_size_vec(2));
    const int depth = internal::SubtleMustCopy(image_size_vec(3));
    OP_REQUIRES_ASYNC(
        context, image_height > 0 && image_width > 0,
        errors::InvalidArgument("image dimensions must be positive"), done);
    OP_REQUIRES_ASYNC(
        context, grads.dim_size(3) == depth,
        errors::InvalidArgument("image_size and grads are incompatible"), done);

     
    Tensor* output = nullptr;
    OP_REQUIRES_OK_ASYNC(
        context,
        context->allocate_output(
            0, TensorShape({batch_size, image_height, image_width, depth}),
            &output),
        done);

    auto compute_callback = [this, context, output]() {
      const Tensor& grads = context->input(0);
      const Tensor& boxes = context->input(1);
      const Tensor& box_index = context->input(2);
      const bool status = functor::CropAndResizeBackpropImage<Device, T>()(
          context, grads.tensor<float, 4>(), boxes.tensor<float, 2>(),
          box_index.tensor<int32, 1>(), output->tensor<T, 4>(), method_);

      if (!status) {
        context->SetStatus(errors::Internal(
            "Failed launch CropAndResizeBackpropImage kernel."));
      }
    };

    RunIfBoxIndexIsValid<Device>(context, box_index.tensor<int32, 1>(),
                                 batch_size, std::move(compute_callback),
                                 std::move(done));
  }