* <p>Unlike {@link Float#parseFloat(String)}, this method returns {@code null} instead of
   * throwing an exception if parsing fails. Valid inputs are exactly those accepted by {@link
   * Float#valueOf(String)}, except that leading and trailing whitespace is not permitted.
   *
   * <p>This implementation is likely to be faster than {@code Float.parseFloat} if many failures
   * are expected.
   *

   * <p>Unlike {@link Double#parseDouble(String)}, this method returns {@code null} instead of
   * throwing an exception if parsing fails. Valid inputs are exactly those accepted by {@link
   * Double#valueOf(String)}, except that leading and trailing whitespace is not permitted.
   *
   * <p>This implementation is likely to be faster than {@code Double.parseDouble} if many failures
   * are expected.
   *

  * Note that NumPy's type promotion rules have been changed(See [NEP 50](https://numpy.org/neps/nep-0050-scalar-promotion.html#nep50)for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results.
  * Tensorflow will continue to support NumPy 1.26 until 2025, aligning with community standard deprecation timeline [here](https://scientific-python.org/specs/spec-0000/).

                tailIndex = newIndex;
                tail = e;
              }
            }
            newTable.set(tailIndex, tail);

            // Clone nodes leading up to the tail.
            for (E e = head; e != tail; e = e.getNext()) {
              int newIndex = e.getHash() & newMask;
              E newNext = newTable.get(newIndex);

                tailIndex = newIndex;
                tail = e;
              }
            }
            newTable.set(tailIndex, tail);

            // Clone nodes leading up to the tail.
            for (E e = head; e != tail; e = e.getNext()) {
              int newIndex = e.getHash() & newMask;
              E newNext = newTable.get(newIndex);

                tailIndex = newIndex;
                tail = e;
              }
            }
            newTable.set(tailIndex, tail);

            // Clone nodes leading up to the tail.
            for (ReferenceEntry<K, V> e = head; e != tail; e = e.getNext()) {
              int newIndex = e.getHash() & newMask;
              ReferenceEntry<K, V> newNext = newTable.get(newIndex);

                tailIndex = newIndex;
                tail = e;
              }
            }
            newTable.set(tailIndex, tail);

            // Clone nodes leading up to the tail.
            for (ReferenceEntry<K, V> e = head; e != tail; e = e.getNext()) {
              int newIndex = e.getHash() & newMask;
              ReferenceEntry<K, V> newNext = newTable.get(newIndex);

</pre>

<p>
For backward compatibility, an imaginary literal's integer part consisting
entirely of decimal digits (and possibly underscores) is considered a decimal
integer, even if it starts with a leading <code>0</code>.
</p>

<pre>
0i
0123i         // == 123i for backward-compatibility
0o123i        // == 0o123 * 1i == 83i
0xabci        // == 0xabc * 1i == 2748i
0.i
2.71828i
1.e+0i