*
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

      LinearTransformation fit = accumulator.leastSquaresFit();
      LinearTransformation fitByAddAllPartitionedPairedStats =
          accumulatorByAddAllPartitionedPairedStats.leastSquaresFit();
      if (values.hasAnyNonFinite()) {
        assertLinearTransformationNaN(fit);
        assertLinearTransformationNaN(fitByAddAllPartitionedPairedStats);
      } else {

      LinearTransformation fit = accumulator.leastSquaresFit();
      LinearTransformation fitByAddAllPartitionedPairedStats =
          accumulatorByAddAllPartitionedPairedStats.leastSquaresFit();
      if (values.hasAnyNonFinite()) {
        assertLinearTransformationNaN(fit);
        assertLinearTransformationNaN(fitByAddAllPartitionedPairedStats);
      } else {

      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      LinearTransformation fit = stats.leastSquaresFit();
      if (values.hasAnyNonFinite()) {
        assertLinearTransformationNaN(fit);
      } else {
        assertDiagonalLinearTransformation(
            fit,
            stats.xStats().mean(),
            stats.yStats().mean(),
            stats.xStats().populationVariance(),

      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      LinearTransformation fit = stats.leastSquaresFit();
      if (values.hasAnyNonFinite()) {
        assertLinearTransformationNaN(fit);
      } else {
        assertDiagonalLinearTransformation(
            fit,
            stats.xStats().mean(),
            stats.yStats().mean(),
            stats.xStats().populationVariance(),

        return logFloor + lessThanBranchFree(cmp, x);

      default:
        throw new AssertionError();
    }
  }

  /** The biggest half power of two that can fit in an unsigned int. */
  @VisibleForTesting static final int MAX_POWER_OF_SQRT2_UNSIGNED = 0xB504F333;

  /**
   * Returns the base-10 logarithm of {@code x}, rounded according to the specified rounding mode.
   *

	// If either the uncompressed or compressed size of the file
	// does not fit in 32 bits, CompressedSize is set to ^uint32(0).
	//
	// Deprecated: Use CompressedSize64 instead.
	CompressedSize uint32

	// UncompressedSize is the compressed size of the file in bytes.
	// If either the uncompressed or compressed size of the file
	// does not fit in 32 bits, CompressedSize is set to ^uint32(0).
	//

    if (in.read() == -1) {
      // oh, there's the end of the stream
      return combineBuffers(bufs, MAX_ARRAY_LEN);
    } else {
      throw new OutOfMemoryError("input is too large to fit in a byte array");
    }
  }

  private static byte[] combineBuffers(Queue<byte[]> bufs, int totalLen) {
    if (bufs.isEmpty()) {
      return new byte[0];
    }
    byte[] result = bufs.remove();

	return arm64.IsAtomicInstruction(op)
}

// IsARM64TBL reports whether the op (as defined by an arm64.A*
// constant) is one of the TBL-like instructions and one of its
// inputs does not fit into prog.Reg, so require special handling.
func IsARM64TBL(op obj.As) bool {
	switch op {
	case arm64.AVTBL, arm64.AVTBX, arm64.AVMOVQ:
		return true
	}
	return false
}