*TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

		bi := BucketInfo{
			Name:    volInfo.Name,
			Created: volInfo.Created,
		}
		if vi, ok := deletedBuckets.Load(volInfo.Name); ok {
			bi.Deleted = vi.Created
		}
		buckets = append(buckets, bi)
		return true
	})

	deletedBuckets.Range(func(_ string, v VolInfo) bool {
		if _, ok := healBuckets.Load(v.Name); !ok {
			buckets = append(buckets, BucketInfo{
				Name:    v.Name,
				Deleted: v.Created,
			})

            .concurrencyLevel(segments)
            .maximumSize(maximumSize)
            .build(
                new CacheLoader<Integer, Integer>() {
                  @Override
                  public Integer load(Integer from) {
                    return (int) misses.incrementAndGet();
                  }
                });

    // To start, fill up the cache.

  private GraphConstants() {}

  static final int EXPECTED_DEGREE = 2;

  static final int DEFAULT_NODE_COUNT = 10;
  static final int DEFAULT_EDGE_COUNT = DEFAULT_NODE_COUNT * EXPECTED_DEGREE;

  // Load factor and capacity for "inner" (i.e. per node/edge element) hash sets or maps
  static final float INNER_LOAD_FACTOR = 1.0f;
  static final int INNER_CAPACITY = 2; // ceiling(EXPECTED_DEGREE / INNER_LOAD_FACTOR)

        updateProperties(fetchedVersions)
    }

    private
    fun getPluginIds(): List<String> {
        val props = Properties()
        propertiesFile.get().asFile.reader().use { input ->
            props.load(input)
        }
        return props.keys.map { it as String }.sorted()
    }

    private
    fun fetchLatestVersions(pluginIds: List<String>): List<TestedVersion> =
        pluginIds

  healthcheck:
    test: ["CMD", "mc", "ready", "local"]
    interval: 5s
    timeout: 5s
    retries: 5

# starts 4 docker containers running minio server instances.
# using nginx reverse proxy, load balancing, you can access
# it through port 9000.
services:
  minio1:
    <<: *minio-common
    hostname: minio1
    volumes:
      - pdata1-1:/pdata1
      - pdata1-2:/pdata2

  minio2:

        Properties props = new Properties();
        InputStream is = new FileInputStream(input);
        try {
            props.load(is);
        } finally {
            is.close();
        }
        loadBuildSrcVersion(props, providerFactory);
        return props;
    }

	// Hence an immediate Read() on the file can return incorrect data.
	if b.canClose != nil {
		b.canClose.Wait()
	}

	// Recycle the buffer.
	if b.byteBuf != nil {
		globalBytePoolCap.Load().Put(b.byteBuf)
		b.byteBuf = nil
	}
	return err
}

// newStreamingBitrotWriterBuffer returns streaming bitrot writer implementation.
// The output is written to the supplied writer w.

   * next entry in the bucket chain). The pointers in [size(), entries.length) are all "null"
   * (UNSET).
   */
  @VisibleForTesting transient long[] entries;

  /** The load factor. */
  private transient float loadFactor;

  /** When we have this many elements, resize the hashtable. */
  private transient int threshold;

func (r *ReplicationStats) getNodeQueueStats(bucket string) (qs ReplQNodeStats) {
	qs.NodeName = globalLocalNodeName
	qs.Uptime = UTCNow().Unix() - globalBootTime.Unix()
	grs := globalReplicationStats.Load()
	if grs != nil {
		qs.ActiveWorkers = grs.ActiveWorkers()
	} else {
		qs.ActiveWorkers = ActiveWorkerStat{}
	}
	qs.XferStats = make(map[RMetricName]XferStats)
	qs.QStats = r.qCache.getBucketStats(bucket)