}

// TrimLeftFunc treats s as UTF-8-encoded bytes and returns a subslice of s by slicing off
// all leading UTF-8-encoded code points c that satisfy f(c).
func TrimLeftFunc(s []byte, f func(r rune) bool) []byte {
	i := indexFunc(s, f, false)
	if i == -1 {
		return nil
	}
	return s[i:]
}

// TrimRightFunc returns a subslice of s by slicing off all trailing
// UTF-8-encoded code points c that satisfy f(c).

The <i>capacity</i> is a measure of that extent: it is the sum of
the length of the slice and the length of the array beyond the slice;
a slice of length up to that capacity can be created by
<a href="#Slice_expressions"><i>slicing</i></a> a new one from the original slice.
The capacity of a slice <code>a</code> can be discovered using the
built-in function <a href="#Length_and_capacity"><code>cap(a)</code></a>.
</p>

<p>

The <i>capacity</i> is a measure of that extent: it is the sum of
the length of the slice and the length of the array beyond the slice;
a slice of length up to that capacity can be created by
<a href="#Slice_expressions"><i>slicing</i></a> a new one from the original slice.
The capacity of a slice <code>a</code> can be discovered using the
built-in function <a href="#Length_and_capacity"><code>cap(a)</code></a>.
</p>

<p>

# Erasure code sizing guide

## Toy Setups

Capacity constrained environments, MinIO will work but not recommended for production.

| servers | drives (per node) | stripe_size | parity chosen (default) | tolerance for reads (servers) | tolerance for writes (servers) |
|--------:|------------------:|------------:|------------------------:|------------------------------:|-------------------------------:|

> NOTE:  While MinIO does not implement an upper boundary on buckets, your cluster's hardware has natural limits that depend on the workload and its scaling patterns. We strongly recommend [MinIO SUBNET](https://min.io/pricing) for architecture and sizing guidance for your production use case.

## List of Amazon S3 API's not supported on MinIO

        *   `tf.data.experimental.OptimizationOptions.map_vectorization` *
            `tf.data.experimental.OptimizationOptions.reorder_data_discarding_ops`
*   `tf.keras`:
    *   Fix usage of `__getitem__` slicing in Keras Functional APIs when the
        inputs are `RaggedTensor` objects.
    *   Add `keepdims` argument to all `GlobalPooling` layers.
    *   Add `include_preprocessing` argument to `MobileNetV3` architectures to

	lock                 sync.Mutex
	bytesSinceLastWindow uint64    // Total bytes since last window was processed
	startTime            time.Time // Start time for window
	expMovingAvg         float64   // Previously calculate sliding window
}

// newBucketMeasurement creates a new instance of the measurement with the initial start time.
func newBucketMeasurement(initTime time.Time) *bucketMeasurement {
	return &bucketMeasurement{

func TestSipHashMod(t *testing.T) {
	testCases := []struct {
		objectName string
		sipHash    int
	}{
		// cases which should pass the test.
		// passing in valid object name.
		{"object", 37},
		{"The Shining Script <v1>.pdf", 38},
		{"Cost Benefit Analysis (2009-2010).pptx", 59},
		{"117Gn8rfHL2ACARPAhaFd0AGzic9pUbIA/5OCn5A", 35},
		{"SHØRT", 49},
		{"There are far too many object names, and far too few bucket names!", 8},

*/

.screenshot {
  display: block;
  background-color: #d3e0de;
  border-radius: 4px;
  padding: 45px 5px 5px;
  position: relative;
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
}

.screenshot img {
  display: block;
  border-radius: 2px;
}

.screenshot:before {
  content: '';
  position: absolute;
  top: 15px;
  left: 15px;

 *
 * Doing it correctly would require running a Gradle build with the full
 * distribution and extracting the generated api jar from its Gradle user home,
 * slowing down building documentation.
 *
 * All this would be so much simpler if the Kotlin extensions to the Gradle API
 * were generated at build time instead.
 *