MinMaxPriorityQueue<Integer> q = MinMaxPriorityQueue.create(initial);
    assertIntact(q);
    q.remove(9);
    q.remove(11);
    q.remove(10);
    // Now we're in the critical state: [1, 15, 13, 8, 14]
    // Removing 8 while iterating caused duplicates in iteration result.
    List<Integer> result = Lists.newArrayListWithCapacity(initial.size());
    for (Iterator<Integer> iter = q.iterator(); iter.hasNext(); ) {

 *
 * <p>The iteration ordering of the collections generated by {@code keySet}, {@code keys}, and
 * {@code asMap} has a few subtleties. As long as the set of keys remains unchanged, adding or
 * removing mappings does not affect the key iteration order. However, if you remove all values
 * associated with a key and then add the key back to the multimap, that key will come last in the
 * key iteration order.
 *

      }
      // localEntries is an alias for the entries field, except if we end up removing duplicates in
      // a copy of the entries array. Likewise, localSize is the same as size except in that case.
      // It's possible to keep using this Builder after calling buildKeepingLast(), so we need to
      // ensure that its state is not corrupted by removing duplicates that should cause a later
      // buildOrThrow() to fail, or by changing the size.

	<code>A</code> must have all methods of <code>C</code>, if any,
	and corresponding method types must unify exactly.
</li>
</ul>

<p>
When solving type equations from type constraints,
solving one equation may infer additional type arguments,
which in turn may enable solving other equations that depend
on those type arguments.
Type inference repeats type unification as long as new type
arguments are inferred.
</p>

   * a no-op, and in some circumstances {@code this} itself is returned. However, if this instance
   * is a {@link #subArray} view of a larger array, this method will copy only the appropriate range
   * of values, resulting in an equivalent array with a smaller memory footprint.
   */
  public ImmutableIntArray trimmed() {
    return isPartialView() ? new ImmutableIntArray(toArray()) : this;
  }

  private boolean isPartialView() {

                    context.logger.info(
                            "Making this build non-interactive, because the environment variable CI equals \"true\"."
                                    + " Disable this detection by removing that variable or adding --force-interactive.");
                    return false;
                }
            }
        }
        return proposedInteractive;
    }

### Policy

An IAM policy in JSON format that you want to use as an inline session policy. This parameter is optional. Passing policies to this operation returns new temporary credentials. The resulting session's permissions are the intersection of the canned policy name and the policy set here. You cannot use this policy to grant more permissions than those allowed by the canned policy name being assumed.

      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

import java.util.function.Consumer;
import javax.annotation.CheckForNull;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * CompactHashSet is an implementation of a Set. All optional operations (adding and removing) are
 * supported. The elements can be any objects.
 *
 * <p>{@code contains(x)}, {@code add(x)} and {@code remove(x)}, are all (expected and amortized)

*   Fixes a set of vulnerabilities caused by using insecure temporary files
    ([CVE-2022-23563](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23563))
*   Fixes an integer overflow in Range resulting in undefined behavior and OOM
    ([CVE-2022-23562](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23562))
*   Fixes a vulnerability where missing validation causes `tf.sparse.split` to