}

    @Override
    public int hashCode() {
      assertTrue(Thread.holdsLock(mutex));
      return delegate.hashCode();
    }

    @Override
    public int size() {
      assertTrue(Thread.holdsLock(mutex));
      return delegate.size();
    }

    @Override
    public boolean isEmpty() {
      assertTrue(Thread.holdsLock(mutex));
      return delegate.isEmpty();
    }

    @Override

      // We explicitly don't lock for iterator()
      assertFalse(Thread.holdsLock(mutex));
      return delegate.iterator();
    }

    @Override
    public int size() {
      assertTrue(Thread.holdsLock(mutex));
      return delegate.size();
    }

    @Override
    public boolean removeAll(Collection<?> collection) {
      assertTrue(Thread.holdsLock(mutex));
      return delegate.removeAll(collection);
    }

* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

	Start, End int64
}

// GetLength - get length of range
func (h *HTTPRangeSpec) GetLength(resourceSize int64) (rangeLength int64, err error) {
	switch {
	case resourceSize < 0:
		return 0, errors.New("Resource size cannot be negative")

	case h == nil:
		rangeLength = resourceSize

	case h.IsSuffixLength:
		specifiedLen := -h.Start
		rangeLength = min(specifiedLen, resourceSize)

	case h.Start >= resourceSize:

    }

    @Override
    Collection<V> createValues() {
      return Collections2.transform(set, function);
    }

    @Override
    public int size() {
      return backingSet().size();
    }

    @Override
    public boolean containsKey(@Nullable Object key) {
      return backingSet().contains(key);
    }

    @Override

		Code:           "EntityTooSmall",
		Description:    "Your proposed upload is smaller than the minimum allowed object size.",
		HTTPStatusCode: http.StatusBadRequest,
	},
	ErrEntityTooLarge: {
		Code:           "EntityTooLarge",
		Description:    "Your proposed upload exceeds the maximum allowed object size.",
		HTTPStatusCode: http.StatusBadRequest,
	},
	ErrExcessData: {
		Code:           "ExcessData",

 * it delegates to other components that potentially encounter problems. Then, the problem reporter can focus on
 * providing a simple error message, leaving the donkey work of creating a nice model problem to this component.
 *
 * @deprecated use {@code org.apache.maven.api.services.ModelBuilder} instead
 */
@Deprecated(since = "4.0.0")

 * under the License.
 */
package org.apache.maven.model.building;

/**
 * Describes a problem that was encountered during model building. A problem can either be an exception that was thrown
 * or a simple string message. In addition, a problem carries a hint about its source, e.g. the POM file that exhibits
 * the problem.
 *
 * @deprecated use {@code org.apache.maven.api.services.ModelBuilder} instead
 */

The `TestClient` is based on <a href="https://www.python-httpx.org" class="external-link" target="_blank">HTTPX</a>, and luckily, we can use it directly to test the API.

## Example { #example }

For a simple example, let's consider a file structure similar to the one described in [Bigger Applications](../tutorial/bigger-applications.md){.internal-link target=_blank} and [Testing](../tutorial/testing.md){.internal-link target=_blank}:

```

/**
 * Implementation of {@code Table} whose iteration ordering across row keys is sorted by their
 * natural ordering or by a supplied comparator. Note that iterations across the columns keys for a
 * single row key may or may not be ordered, depending on the implementation. When rows and columns
 * are both sorted, it's easier to use the {@link TreeBasedTable} subclass.
 *