if (!isa<func::FuncOp, IfOp, WhileOp>(contraction->getParentOp())) {
      return rewriter.notifyMatchFailure(
          contraction,
          "fused operation must be nested inside a function, If or While");
    }

    // If the contraction is used in multiple places, fusing it will only create
    // more contraction nodes, which is slower.
    if (!contraction.getResult().hasOneUse())

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

        // Checks that matrix multiply can perform a valid contraction.
        if (x_col != y_row) {
          result_shape.clear();
          return false;
        }

        result_shape.push_back(x_row);
        result_shape.push_back(y_col);
        return true;

        //  c. There is no path from B to A in the cycles graph (but there may
        //     be a path from A to B).
        //
        // So check the legality of the edge contraction by checking if any of
        // the n^2 pairs of resource variable operations are forbidden.
        if (unsafe_resource_deps_.contains(
                {resource_var_from, resource_var_to})) {

  let hasVerifier = 1;
}

def TF_EinsumOp : TF_Op<"Einsum", [Pure]> {
  let summary = [{
Tensor contraction according to Einstein summation convention.
  }];

  let description = [{
Implements generalized Tensor contraction and reduction. Each input Tensor must
have a corresponding input subscript appearing in the comma-separated left-hand

        `batch_dims` case.
    *   Removing of dtype in the constructor of initializers and partition_info
        in call.
    *   Add `tf.math.nextafter` op.
    *   Turn on MKL-DNN contraction kernels by default. MKL-DNN dynamically
        dispatches the best kernel implementation based on CPU vector
        architecture. To disable them, build with
        `--define=tensorflow_mkldnn_contraction_kernel=0`.