// it (e.g. conds instead of ifs). The value shouldn't really matter.
      TF_RETURN_IF_ERROR(vspace_.BuildOnesLike(output_tensor, &aid));
    }
    if (TF_PREDICT_FALSE(aid == nullptr)) {
      return tensorflow::errors::Internal(
          "Failed to create ones tensor for tensor ", output_tensor.GetID(),
          " with dtype ", output_tensor.GetDType());
    }

    *   `Model.fit_generator`, `Model.evaluate_generator`,
        `Model.predict_generator`, `Model.train_on_batch`,
        `Model.test_on_batch`, and `Model.predict_on_batch` methods now respect
        the `run_eagerly` property, and will correctly run using `tf.function`
        by default. Note that `Model.fit_generator`, `Model.evaluate_generator`,
        and `Model.predict_generator` are deprecated endpoints. They are

        "//learning/lib/ami/simple_ml/...",
        "//learning/pathways/...",
        "//learning/serving/contrib/tfrt/mlir/canonical_ops/...",
        "//learning/serving/experimental/remote_predict/...",
        "//perftools/accelerators/xprof/convert/...",
        "//perftools/accelerators/xprof/integration_tests/...",
        "//smartass/brain/configure/...",
        "//tensorflow/...",

/**
 * Legacy version of {@link java.util.function.Predicate java.util.function.Predicate}. Determines a
 * true or false value for a given input.
 *
 * <p>As this interface extends {@code java.util.function.Predicate}, an instance of this type may
 * be used as a {@code Predicate} directly. To use a {@code java.util.function.Predicate} where a
 * {@code com.google.common.base.Predicate} is expected, use the method reference {@code

   *       Objects.equal}{@code (a, b)} implies that {@code predicate.apply(a) ==
   *       predicate.apply(b))}.
   * </ul>
   *
   * @throws NullPointerException if {@code input} is null and this predicate does not accept null
   *     arguments
   */
  boolean apply(@ParametricNullness T input);

  /**
   * Indicates whether another object is equal to this predicate.
   *

      Predicate<E> combinedPredicate = Predicates.and(filtered.predicate, predicate);
      return new FilteredSet<>((Set<E>) filtered.unfiltered, combinedPredicate);
    }

    return new FilteredSet<>(checkNotNull(unfiltered), checkNotNull(predicate));
  }

  /**
   * Returns the elements of a {@code SortedSet}, {@code unfiltered}, that satisfy a predicate. The

//     Those cannot be well described when embedded.
//  3. Inconsistent validation.  Because the usages are different, the validation rules are different by usage, which makes it hard for users to predict what will happen.
//  4. The fields are both imprecise and overly precise.  Kind is not a precise mapping to a URL. This can produce ambiguity

    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):
    result = ml_models["answer_to_everything"](x)
    return {"result": result}
```

**Note**: This is the recommended way going forward, instead of using `startup` and `shutdown` events.

         * The result will contain only the paths of types for which the predicate returned {@code true}.
         * It is recommended to apply a filter for retaining only the types of paths of interest,
         * because it can resolve ambiguities when a path could be of many types.
         *
         * @param pathTypeFilter predicate evaluating whether a path type should be included in the result

    unfiltered.put("horse", 5);
    Predicate<Entry<?, ?>> predicate =
        new Predicate<Entry<?, ?>>() {
          @Override
          public boolean apply(Entry<?, ?> input) {
            return "cat".equals(input.getKey()) || Integer.valueOf(2) == input.getValue();
          }
        };
    Map<String, Integer> filtered = Maps.filterEntries(unfiltered, predicate);