var finished: Boolean = false,
  ) : Sink {
    /**
     * Buffer of outgoing data. This batches writes of small writes into this sink as larges frames
     * written to the outgoing connection. Batching saves the (small) framing overhead.
     */
    private val sendBuffer = Buffer()

    /** Trailers to send at the end of the stream. */
    var trailers: Headers? = null

        @Test
        @DisplayName("Should handle buffer overflow protection")
        void testBufferOverflowProtection() {
            request.setReadLength(65536);

            byte[] smallBuffer = new byte[48]; // Smaller than required 49 bytes

            // Should not overflow buffer
            assertThrows(ArrayIndexOutOfBoundsException.class, () -> request.writeBytesWireFormat(smallBuffer, 0));
        }

        @Test

                    new Smb2Lock(200L, 300L, Smb2Lock.SMB2_LOCKFLAG_SHARED_LOCK) };
            Smb2LockRequest req = new Smb2LockRequest(mockConfig, testFileId, locks);

            byte[] smallBuffer = new byte[50]; // Smaller than required

            // Should not overflow buffer
            assertThrows(ArrayIndexOutOfBoundsException.class, () -> req.writeBytesWireFormat(smallBuffer, 0));
        }

        @Test

      return actions[random.nextInt(actions.length)];
    }
  }

  /**
   * Test that the hash function contains no funnels. A funnel is a situation where a set of input
   * (key) bits 'affects' a strictly smaller set of output bits. Funneling is bad because it can
   * result in more-than-ideal collisions for a non-uniformly distributed key space. In practice,

   * href="http://mathworld.wolfram.com/PrimeNumber.html">prime number</a>: an integer <i>greater
   * than one</i> that cannot be factored into a product of <i>smaller</i> positive integers.
   * Returns {@code false} if {@code n} is zero, one, or a composite number (one which <i>can</i> be
   * factored into smaller positive integers).
   *
   * <p>To test larger numbers, use {@link BigInteger#isProbablePrime}.
   *

      Iterable<? extends Iterable<? extends T>> inputs) {
    return FluentIterable.concat(inputs);
  }

  /**
   * Divides an iterable into unmodifiable sublists of the given size (the final iterable may be
   * smaller). For example, partitioning an iterable containing {@code [a, b, c, d, e]} with a
   * partition size of 3 yields {@code [[a, b, c], [d, e]]} -- an outer iterable containing two

   * this is not possible.
   *
   * <p>This method, in contrast to {@link #remove(Object, int)}, has no effect when the element
   * count is smaller than {@code occurrences}.
   *
   * @param element the element to remove
   * @param occurrences the number of occurrences of {@code element} to remove

navigate to the
[GitHub "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start
looking through interesting issues. If you are not sure of where to start, then
start by trying one of the smaller/easier issues here i.e.
[issues with the "good first issue" label](https://github.com/tensorflow/tensorflow/labels/good%20first%20issue)
and then take a look at the

    if (size < requireEntries().length) {
      resizeEntries(size);
    }
    int minimumTableSize = CompactHashing.tableSize(size);
    int mask = hashTableMask();
    if (minimumTableSize < mask) { // smaller table size will always be less than current mask
      resizeTable(mask, minimumTableSize, UNSET, UNSET);
    }
  }

  @Override
  public void clear() {
    if (needsAllocArrays()) {
      return;

		return 0, 0, nil
	}
	partOffset = offset
	// Seek until object offset maps to a particular part offset.
	for i, part := range fi.Parts {
		partIndex = i
		// Offset is smaller than size we have reached the proper part offset.
		if partOffset < part.Size {
			return partIndex, partOffset, nil
		}
		// Continue to towards the next part.
		partOffset -= part.Size
	}