// Build new BackPropFilterOp.
  auto loc = backprop.getLoc();
  auto new_backprop = builder.create<TF::Conv2DBackpropFilterOp>(
      loc, new_result_type, input, new_filter_sizes, backprop.getOutBackprop(),
      strides, backprop.getUseCudnnOnGpu(), backprop.getPadding(),
      backprop.getExplicitPaddings(), backprop.getDataFormat(),
      backprop.getDilations());

      CreateCastToInt32(tf_op.getInputSizes(), op->getLoc(), rewriter);

  rewriter.replaceOpWithNewOp<TFL::Conv3DTransposeOp>(
      op, tf_op.getType(), output_shape, tf_op.getFilter(),
      tf_op.getOutBackprop(),
      /*bias=*/none, dilation_depth_factor, dilation_height_factor,
      dilation_width_factor,
      /*fused_activation_function=*/rewriter.getStringAttr("NONE"), padding,

    if (!HasRankAtLeast(op.getOutBackprop(), 2))
      return op.emitOpError(
          "requires out_backprop operand to have rank at least two with `NHWC` "
          "data format");
  } else {
    // Op definition requires data_format to be either NHWC or NCHW.
    DCHECK_EQ(format, tensorflow::TensorFormat::FORMAT_NCHW);
    if (!HasRankAtLeast(op.getOutBackprop(), 3))
      return op.emitOpError(

    tensorflow::Padding padding;
    if (!GetPaddingFromString(op.getPadding().str(), &padding).ok())
      return failure();

    auto out_backprop_ty =
        mlir::dyn_cast<RankedTensorType>(op.getOutBackprop().getType());
    auto filter_ty = mlir::dyn_cast<RankedTensorType>(op.getFilter().getType());

    // With the exception of out_backprop's batch dimension, out_backprop and