}

	// Now, we read the payload and compute its SHA-256 hash.
	_, err = io.ReadFull(cr.reader, cr.buffer)
	if err == io.EOF && size != 0 {
		err = io.ErrUnexpectedEOF
	}
	if err != nil && err != io.EOF {
		cr.err = err
		return n, cr.err
	}

	// Once we have read the entire chunk successfully, we verify
	// that the received signature matches our computed signature.
	cr.chunkSHA256Writer.Write(cr.buffer)

   *   <li>If {@code x} is positive infinity, the result is positive infinity.
   *   <li>If {@code x} is positive or negative zero, the result is negative infinity.
   * </ul>
   *
   * <p>The computed result is within 1 ulp of the exact result.
   *
   * <p>If the result of this method will be immediately rounded to an {@code int}, {@link
   * #log2(double, RoundingMode)} is faster.
   */

                 TF_Message(first_bad_status.get()));
    return result;
  }
  // For each output of the original operation, pack the per-device
  // TensorHandles we've computed into a single parallel TensorHandle.
  std::vector<std::unique_ptr<ParallelTensor>> per_device_outputs;
  per_device_outputs.reserve(first_op_output_count);
  for (int i = 0; i < first_op_output_count; ++i) {

  }

  /**
   * Splits {@code sequence} into string components and makes them available through an {@link
   * Iterator}, which may be lazily evaluated. If you want an eagerly computed {@link List}, use
   * {@link #splitToList(CharSequence)}.
   *
   * @param sequence the sequence of characters to split
   * @return an iteration over the segments split from the parameter
   */

		}
		if equal := Equal(A, B); equal != test.Equal {
			t.Fatalf("Test %d: got %v - want %v", i, equal, test.Equal)
		}
	}
}

var readerTests = []struct { // Reference values computed by: echo <content> | md5sum
	Content string
	ETag    ETag
}{
	{
		Content: "", ETag: ETag{212, 29, 140, 217, 143, 0, 178, 4, 233, 128, 9, 152, 236, 248, 66, 126},
	},
	{

    int approxSize = IntMath.divide(n * IntMath.log2(n, CEILING), Long.SIZE, CEILING);
    ArrayList<BigInteger> bignums = new ArrayList<>(approxSize);

    // Start from the pre-computed maximum long factorial.
    int startingNumber = LongMath.factorials.length;
    long product = LongMath.factorials[startingNumber - 1];
    // Strip off 2s from this value.

     * @return the system properties, never {@code null}
     */
    @Nonnull
    Map<String, String> getSystemProperties();

    /**
     * Each invocation computes a new map of effective properties. To be used in interpolation.
     * <p>
     * Effective properties are computed from system, user and optionally project properties, layered with
     * defined precedence onto each other to achieve proper precedence. Precedence is defined as:

data.offsets.reference,\n    this.popper,\n    this.reference,\n    this.options.modifiers.flip.boundariesElement,\n    this.options.modifiers.flip.padding\n  );\n\n  // store the computed placement inside `originalPlacement`\n  data.originalPlacement = data.placement;\n\n  data.positionFixed = this.options.positionFixed;\n\n  // compute the popper offsets\n  data.offsets.popper = getPopperOffsets(\n    this.popper,\n    data.offsets.reference,\n    data.placement\n  );\n\n  data.offsets.popper.position =...

import java.nio.ByteOrder;
import java.util.Iterator;
import javax.annotation.CheckForNull;

/**
 * A bundle of statistical summary values -- sum, count, mean/average, min and max, and several
 * forms of variance -- that were computed from a single set of zero or more floating-point values.
 *
 * <p>There are two ways to obtain a {@code Stats} instance:
 *
 * <ul>

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