"requires non negative explicit paddings");
  }

  ArrayRef<Attribute> strides = op.getStrides().getValue();
  ArrayRef<Attribute> dilations = op.getDilations().getValue();
  if (failed(
          VerifyConvOpAttributes(num_dims, strides, dilations, op.getLoc()))) {
    return failure();
  }

  int64_t input_channels = ShapedType::kDynamic;

      loc, new_result_type, input, new_filter_sizes, backprop.getOutBackprop(),
      strides, backprop.getUseCudnnOnGpu(), backprop.getPadding(),
      backprop.getExplicitPaddings(), backprop.getDataFormat(),
      backprop.getDilations());

  // For example, if new filter shape is [4, 4, 12, 64], old filter shape
  // is [7, 7, 3, 64] with block_size 2.
  // Below transforms will be applied to the filter:
  // 1. Reshape to [4, 4, 2, 2, 3, 64];

    // implemented.
    if (input_ty.hasStaticShape() && filter_ty.hasStaticShape())
      return failure();

    ArrayRef<Attribute> dilations = op.getDilations().getValue();
    ArrayRef<Attribute> strides = op.getStrides().getValue();
    ArrayRef<Attribute> explicit_paddings;
    if (padding == tensorflow::Padding::EXPLICIT) {