Value input_rhs = bmm_op.getY();

    Value output_lhs =
        bmm_op.getAdjX() ? create_z_x_transpose_op(input_lhs) : input_lhs;

    // The rhs need to be transposed if adj_y == false AND this matmul will be
    // legalized to tfl.fully_connected
    Value output_rhs =
        !bmm_op.getAdjY() ? create_z_x_transpose_op(input_rhs) : input_rhs;

    Type output_type = bmm_op.getResult().getType();

        }

        const int x_row =
            matmul_op.getAdjX() ? shape_x.back() : *(shape_x.rbegin() + 1);
        const int x_col =
            !matmul_op.getAdjX() ? shape_x.back() : *(shape_x.rbegin() + 1);

        const int y_row =
            matmul_op.getAdjY() ? shape_y.back() : *(shape_y.rbegin() + 1);
        const int y_col =
            !matmul_op.getAdjY() ? shape_y.back() : *(shape_y.rbegin() + 1);

    if (dims_a < 2 || dims_b < 2) {
      return failure();
    }

    // einsum equation for batchmatmul
    std::string equation("...mk,...kn->...mn");
    if (op.getAdjX()) std::swap(equation[3], equation[4]);
    if (op.getAdjY()) std::swap(equation[6 + 3], equation[6 + 4]);

    rewriter.replaceOpWithNewOp<TF::EinsumOp>(
        op, op.getType(),
        /*inputs=*/ValueRange({input_lhs, input_rhs}),