/**
   * Returns the count of {@code node}'s incident edges, counting self-loops twice (equivalently,
   * the number of times an edge touches {@code node}).
   *
   * <p>For directed graphs, this is equal to {@code inDegree(node) + outDegree(node)}.
   *
   * <p>For undirected graphs, this is equal to {@code incidentEdges(node).size()} + (number of
   * self-loops incident to {@code node}).
   *

  /**
   * Returns the count of {@code node}'s incident edges, counting self-loops twice (equivalently,
   * the number of times an edge touches {@code node}).
   *
   * <p>For directed graphs, this is equal to {@code inDegree(node) + outDegree(node)}.
   *
   * <p>For undirected graphs, this is equal to {@code incidentEdges(node).size()} + (number of
   * self-loops incident to {@code node}).
   *

  /**
   * Returns the count of {@code node}'s incident edges, counting self-loops twice (equivalently,
   * the number of times an edge touches {@code node}).
   *
   * <p>For directed graphs, this is equal to {@code inDegree(node) + outDegree(node)}.
   *
   * <p>For undirected graphs, this is equal to {@code incidentEdges(node).size()} + (number of
   * self-loops incident to {@code node}).
   *

	}
}

// Borrowed from mutex_test.go
func HammerMutex(m *DRWMutex, loops int, cdone chan bool) {
	for range loops {
		m.Lock(id, source)
		m.Unlock(context.Background())
	}
	cdone <- true
}

// Borrowed from mutex_test.go
func TestMutex(t *testing.T) {
	loops := 200
	if testing.Short() {
		loops = 5
	}
	c := make(chan bool)
	m := NewDRWMutex(ds, "test")
	for range 10 {

	v := &g[T]{
		db:  db,
		ops: make([]op, 0, 5),
	}

	if len(opts) > 0 {
		v.ops = append(v.ops, func(db *DB) *DB {
			return db.Clauses(opts...)
		})
	}

	v.createG = &createG[T]{
		chainG: chainG[T]{
			execG: execG[T]{g: v},
		},
	}
	return v
}

type g[T any] struct {
	*createG[T]
	db  *DB
	ops []op
}

  /**
   * Returns the count of {@code node}'s incident edges, counting self-loops twice (equivalently,
   * the number of times an edge touches {@code node}).
   *
   * <p>For directed graphs, this is equal to {@code inDegree(node) + outDegree(node)}.
   *
   * <p>For undirected graphs, this is equal to {@code incidentEdges(node).size()} + (number of
   * self-loops incident to {@code node}).
   *

				consistent = false
				continue
			}
			ver := vers[0]
			if len(tops) == 0 {
				consistent = true
				topSig = ver.header
			} else {
				consistent = consistent && ver.header == topSig
			}
			tops = append(tops, vers[0])
		}

		// Check if done...
		if len(tops) < quorum {
			// We couldn't gather enough for quorum
			break
		}

 * user-defined properties.
 *
 * <p>A {@code Network} built by this class has the following default properties:
 *
 * <ul>
 *   <li>does not allow parallel edges
 *   <li>does not allow self-loops
 *   <li>orders {@link Network#nodes()} and {@link Network#edges()} in the order in which the
 *       elements were added (insertion order)
 * </ul>
 *

    TF_DataType data_type, void* data, size_t len, TF_Status* status);

// Specify the server_def that enables collective ops.
// This is different to the above function in that it doesn't create remote
// contexts, and remotely executing ops is not possible. It just enables
// communication for collective ops.
TF_CAPI_EXPORT extern void TFE_EnableCollectiveOps(TFE_Context* ctx,

#include "tensorflow/c/experimental/filesystem/modular_filesystem.h"
#include "tensorflow/cc/framework/gradients.h"
#include "tensorflow/cc/framework/ops.h"
#include "tensorflow/cc/framework/scope_internal.h"
#include "tensorflow/cc/ops/while_loop.h"
#include "tensorflow/cc/saved_model/loader.h"
#include "tensorflow/core/distributed_runtime/server_lib.h"
#include "tensorflow/core/framework/logging.h"