req.Header.Set(xhttp.MinIOPeerCall, "true")

	resp, err := httpClient.Do(req)
	if err != nil {
		return err
	}
	xhttp.DrainBody(resp.Body)

	return nil
}

// connect to list of endpoints and load all Erasure disk formats, validate the formats are correct
// and are in quorum, if no formats are found attempt to initialize all of them for the first
// time. additionally make sure to close all the disks used in this attempt.

        }
        load();
    }

    /**
     * Updates the processor configuration by reloading available searchers.
     * This method executes the load operation asynchronously in a separate thread.
     */
    public void update() {
        CommonPoolUtil.execute(this::load);
    }

    /**

     *
     * @param <K> the key type of {@link ConcurrentHashMap}
     * @param <V> the value type of {@link ConcurrentHashMap}
     * @param initialCapacity the initial capacity
     * @param loadFactor the load factor threshold for resizing
     * @param concurrencyLevel the estimated number of threads for concurrent updates
     * @return a new instance of {@link ConcurrentHashMap}

st"),e)r.setRequestHeader(n,e[n]);o=function(e){return function(){o&&(o=a=r.onload=r.onerror=r.onabort=r.ontimeout=r.onreadystatechange=null,"abort"===e?r.abort():"error"===e?"number"!=typeof r.status?t(0,"error"):t(r.status,r.statusText):t(Yt[r.status]||r.status,r.statusText,"text"!==(r.responseType||"text")||"string"!=typeof r.responseText?{binary:r.response}:{text:r.responseText},r.getAllResponseHeaders()))}},r.onload=o(),a=r.onerror=r.ontimeout=o("error"),void 0!==r.onabort?r.onabort=a:r.onr...

            .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)

not reloading local variables from shared memory.
For example, a compiler must not discard <code>i</code> and reload it
a second time from <code>*p</code> in this program:
</p>

<pre>
i := *p
if i &lt; 0 || i &gt;= len(funcs) {
	panic("invalid function index")
}
... complex code ...
// compiler must NOT reload i = *p here
funcs[i]()
</pre>

<p>

	// 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.

    </default>
  </defaults>

  <classes>
    <class rootElement="true" xml.tagName="extensions" xsd.compositor="sequence">
      <name>CoreExtensions</name>
      <description>Extensions to load.</description>
      <version>1.0.0+</version>
      <fields>
        <field>
          <name>extensions</name>
          <description>A set of build extensions to use from this project.</description>

   
   
   *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.