*
 * <h3>Examples</h3>
 *
 * <p>To compute the median:
 *
 * {@snippet :
 * double myMedian = median().compute(myDataset);
 * }
 *
 * where {@link #median()} has been statically imported.
 *
 * <p>To compute the 99th percentile:
 *
 * {@snippet :
 * double myPercentile99 = percentiles().index(99).compute(myDataset);
 * }
 *

/**
 * @author Jesse Wilson
 */
final class Platform {
  static CharMatcher precomputeCharMatcher(CharMatcher matcher) {
    // CharMatcher.precomputed() produces CharMatchers that are maybe a little
    // faster (and that's debatable), but definitely more memory-hungry. We're
    // choosing to turn .precomputed() into a no-op in GWT, because it doesn't
    // seem to be a worthwhile tradeoff in a browser.
    return matcher;
  }

export DEBIAN_FRONTEND=noninteractive

# Fetch the NVIDIA key.
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub;

# Set up sources for NVIDIA CUDNN.
cat >/etc/apt/sources.list.d/nvidia.list <<SOURCES
# NVIDIA
deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /

        return (logX2Floor < 2 * logFloor + 1) ? logFloor : logFloor + 1;
    }
    throw new AssertionError();
  }

  /*
   * The maximum number of bits in a square root for which we'll precompute an explicit half power
   * of two. This can be any value, but higher values incur more class load time and linearly
   * increasing memory consumption.
   */

            print(
                'ERROR: TensorFlow only supports small CUDA compute'
                ' capabilities of sm_30 and higher. Please re-specify the list'
                ' of compute capabilities excluding version %s.' % ver)
            all_valid = False
          if ver < 35:
            print('WARNING: XLA does not support CUDA compute capabilities '

    // Compute the filter.
    long filter = 0;
    int size = chars.cardinality();
    boolean containsZero = chars.get(0);
    // Compute the hash table.
    char[] table = new char[chooseTableSize(size)];
    int mask = table.length - 1;
    for (int c = chars.nextSetBit(0); c != -1; c = chars.nextSetBit(c + 1)) {
      // Compute the filter at the same time.
      filter |= 1L << c;

 
-     The choice of symbol is arbitrary. The static address we obtain
-     by constructing a non GOT reference to the symbol, the dynamic
-     address of the symbol we compute using adrp/add to compute the
-     symbol's address relative to the PC. */
-
-  ElfW(Addr) static_addr;
-  ElfW(Addr) dynamic_addr;
-
-  asm ("					\n\
-	adrp	%1, _dl_start;			\n\

// Copyright 2009 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

//go:build ignore

// Compute Fibonacci numbers with two goroutines
// that pass integers back and forth.  No actual
// concurrency, just threads and synchronization
// and foreign code on multiple pthreads.

package main

import (
	big "."
	"runtime"
)

## Why is MinIO cloud-native?

    this.nextOrNull = firstOrNull;
  }

  /**
   * Returns the element that follows {@code previous}, or returns {@code null} if no elements
   * remain. This method is invoked during each call to {@link #next()} in order to compute the
   * result of a <i>future</i> call to {@code next()}.
   */
  protected abstract @Nullable T computeNext(T previous);

  @Override
  public final boolean hasNext() {
    return nextOrNull != null;
  }