namespace tensorflow {

// Abstract interface to a Tensor handle in either tracing or immediate
// execution mode.
class AbstractTensorHandle : public core::RefCounted {
 protected:
  enum AbstractTensorHandleKind { kGraph, kMlir, kEager, kTfrt, kCustomDevice };
  explicit AbstractTensorHandle(AbstractTensorHandleKind kind) : kind_(kind) {}
  ~AbstractTensorHandle() override {}

 public:
  // Returns tensor dtype.

 *
 * This class may be used to stream very large responses. For example, it is possible to use this
 * class to read a response that is larger than the entire memory allocated to the current process.
 * It can even stream a response larger than the total storage on the current device, which is a
 * common requirement for video streaming applications.
 *

   *
   * @param element the element to prepend to the front of {@code array}
   * @param array the array of elements to append
   * @return an array whose size is one larger than {@code array}, with {@code element} occupying
   *     the first position, and the elements of {@code array} occupying the remaining elements.
   */

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limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_
#define TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/refcount.h"
namespace tensorflow {
namespace tracing {
namespace graph {
using tensorflow::AbstractFunction;

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        if ( a.atMost(b) ) {
            return a;
        }
        return b;
    }


    /**
     * 
     * @param a
     * @param b
     * @return larger of the two versions
     */
    public static DialectVersion max ( DialectVersion a, DialectVersion b ) {
        if ( a.atLeast(b) ) {
            return a;
        }
        return b;
    }

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