for i = 0; i < 8; i++ {
		fi := FileInfo{
			TransitionTier:      inputs[0].tier,
			TransitionedObjName: inputs[0].remoteObjName,
			TransitionVersionID: inputs[0].remoteVersionID,
			TransitionStatus:    inputs[0].status,
		}
		ofi := fi
		if i&(1<<0) != 0 {
			ofi.TransitionTier = inputs[1].tier
		}
		if i&(1<<1) != 0 {
			ofi.TransitionedObjName = inputs[1].remoteObjName
		}
		if i&(1<<2) != 0 {

  @SafeVarargs
  public static <T extends @Nullable Object> Iterator<T> concat(Iterator<? extends T>... inputs) {
    return concatNoDefensiveCopy(Arrays.copyOf(inputs, inputs.length));
  }

  /**
   * Combines multiple iterators into a single iterator. The returned iterator iterates across the
   * elements of each iterator in {@code inputs}. The input iterators are not polled until
   * necessary.
   *

	// since dataInserted === dataFetched back is a primary criteria for any object storage this assertion is critical.
	bytesData := []struct {
		byteData []byte
	}{
		{generateBytesData(6 * humanize.MiByte)},
	}
	// set of inputs for uploading the objects before tests for downloading is done.
	putObjectInputs := []struct {
		bucketName    string
		objectName    string
		contentLength int64
		textData      []byte
		metaData      map[string]string

### Usage

```
minio server --config config.yaml
```

Lets you start MinIO server with all inputs to start MinIO server provided via this configuration file, once the configuration file is provided all other pre-existing values on disk for configuration are overridden by the new values set in this configuration file.

   */
  @SafeVarargs
  public static <T extends @Nullable Object> Iterable<T> concat(Iterable<? extends T>... inputs) {
    return FluentIterable.concat(inputs);
  }

  /**
   * Combines multiple iterables into a single iterable. The returned iterable has an iterator that
   * traverses the elements of each iterable in {@code inputs}. The input iterators are not polled
   * until necessary.
   *

  }

  /**
   * Returns a {@code Collector} that accumulates elements into an {@code ImmutableMultiset} whose
   * elements are the result of applying {@code elementFunction} to the inputs, with counts equal to
   * the result of applying {@code countFunction} to the inputs.
   *
   * <p>If the mapped elements contain duplicates (according to {@link Object#equals}), the first

        StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs):
        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],

  /**
   * Returns a {@code Collector} that accumulates elements into an {@code ImmutableSortedMultiset}
   * whose elements are the result of applying {@code elementFunction} to the inputs, with counts
   * equal to the result of applying {@code countFunction} to the inputs.
   *
   * <p>If the mapped elements contain duplicates (according to {@code comparator}), the first

   * input element is mapped to one cell in the returned table, with the rows, columns, and values
   * generated by applying the specified functions. If multiple inputs are mapped to the same row
   * and column pair, they will be combined with the specified merging function in encounter order.
   *
   * <p>The returned {@code Collector} will throw a {@code NullPointerException} at collection time

// unique set. data is expected to be pre-sorted, and the resulting set in
// the range [0:size] will remain in sorted order. Uniq, following a
// sort.Sort call, can be used to prepare arbitrary inputs for use as sets.
func uniq(data sort.Interface) (size int) {
	p, l := 0, data.Len()
	if l <= 1 {
		return l
	}
	for i := 1; i < l; i++ {
		if !data.Less(p, i) {
			continue
		}
		p++