Directory string

	// MaximumFileSize defines the maximum size of each log file in bytes.
	MaximumFileSize int64

	// FileNameFunc specifies the name a new file will take.
	// FileNameFunc must ensure collisions in filenames do not occur.
	// Do not rely on timestamps to be unique, high throughput writes
	// may fall on the same timestamp.
	// Eg.
	// 	2020-03-28_15-00-945-<random-hash>.log

 * including those in many JDK classes.
 *
 * <p>{@code Object.hashCode} implementations tend to be very fast, but have weak collision
 * prevention and <i>no</i> expectation of bit dispersion. This leaves them perfectly suitable for
 * use in hash tables, because extra collisions cause only a slight performance hit, while poor bit
 * dispersion is easily corrected using a secondary hash function (which all reasonable hash table

 * including those in many JDK classes.
 *
 * <p>{@code Object.hashCode} implementations tend to be very fast, but have weak collision
 * prevention and <i>no</i> expectation of bit dispersion. This leaves them perfectly suitable for
 * use in hash tables, because extra collisions cause only a slight performance hit, while poor bit
 * dispersion is easily corrected using a secondary hash function (which all reasonable hash table

   *
   * <p>This is designed for generating persistent fingerprints of strings. It isn't
   * cryptographically secure, but it produces a high-quality hash with fewer collisions than some
   * alternatives we've used in the past.
   *
   * <p>FarmHash fingerprints are encoded by {@link HashCode#asBytes} in little-endian order. This

	}
	return diskCount
}

// hashOrder - hashes input key to return consistent
// hashed integer slice. Returned integer order is salted
// with an input key. This results in consistent order.
// NOTE: collisions are fine, we are not looking for uniqueness
// in the slices returned.
func hashOrder(key string, cardinality int) []int {
	if cardinality <= 0 {
		// Returns an empty int slice for cardinality < 0.
		return nil

#include "tensorflow/core/platform/errors.h"

namespace tensorflow {
namespace gradients {
namespace {

// TODO(b/172558015): Using the pointer address as the identifier for the tensor
// may lead to collisions. Introduce another way to get a unique id for this
// tensor.
int64_t ToId(const AbstractTensorHandle* t) {
  return static_cast<int64_t>(reinterpret_cast<uintptr_t>(t));
}

Using these ideas, JWT can be used for way more sophisticated scenarios.

In those cases, several of those entities could have the same ID, let's say `foo` (a user `foo`, a car `foo`, and a blog post `foo`).

So, to avoid ID collisions, when creating the JWT token for the user, you could prefix the value of the `sub` key, e.g. with `username:`. So, in this example, the value of `sub` could have been: `username:johndoe`.

   *       </ul>
   *   <li>another java.util.Set delegate implementation. In most modern JDKs, normal java.util hash
   *       collections intelligently fall back to a binary search tree if hash table collisions are
   *       detected. Rather than going to all the trouble of reimplementing this ourselves, we
   *       simply switch over to use the JDK implementation wholesale if probable hash flooding is

To learn more about Python Packages and Modules, read <a href="https://docs.python.org/3/tutorial/modules.html" class="external-link" target="_blank">the official Python documentation about Modules</a>.

///

### Avoid name collisions

We are importing the submodule `items` directly, instead of importing just its variable `router`.

This is because we also have another variable named `router` in the submodule `users`.

   * bulkGet(keys)} operation. See <a href="http://www.mathpages.com/home/kmath199.htm">Balls in
   * Bins model</a> for mathematical formulas that can be used to estimate the probability of
   * collisions.
   *
   * @param keys arbitrary non-null keys
   * @return the stripes corresponding to the objects (one per each object, derived by delegating to