auto new_filter =
        rewriter.create<TF::MulOp>(mul_op.getLoc(), filter, new_const_val)
            .getZ();
    // If bias isn't None, it needs to be multiplied as well.
    if (!mlir::isa<NoneType>(bias.getType())) {
      bias = rewriter.create<TF::MulOp>(mul_op.getLoc(), bias, constant_val)
                 .getZ();
    }

    auto fc = rewriter.create<TFL::FullyConnectedOp>(

      /*weight_zp=*/0,
      /*input_output_dims=*/ArrayRef<int64_t>({0}),
      /*weight_output_dims=*/ArrayRef<int64_t>({num_dims - 1}));
  return builder.create<TF::SubOp>(loc, xla_conv_output, zp_offset).getZ();
}

// Creates a XlaConvV2Op from TF Conv2DOp and returns its output. The returned
// value will be used as an input of the next op.
Value CreateXlaConvOpFromTfConv2dOp(OpBuilder &builder, Location loc,

    if (!l || !r) return failure();

    auto element_type = getElementTypeOrSelf(l.getType());
    if (!element_type.isBF16()) return failure();

    auto out_type = op.getZ().getType();

    l = rewriter.create<ConvertOp>(op.getLoc(), l, rewriter.getF32Type());
    r = rewriter.create<ConvertOp>(op.getLoc(), r, rewriter.getF32Type());