Description:    "The provided 'x-amz-content-sha256' header does not match what was computed.",
		HTTPStatusCode: http.StatusBadRequest,
	},
	ErrContentChecksumMismatch: {
		Code:           "XAmzContentChecksumMismatch",
		Description:    "The provided 'x-amz-checksum' header does not match what was computed.",
		HTTPStatusCode: http.StatusBadRequest,
	},

	// MinIO extensions.
	ErrStorageFull: {

    // Ensure that referenceMap, which doesn't use WithExpectedSize, ends up with the same table
    // size as the other two maps. If it ended up with a smaller size that would imply that we
    // computed the wrong initial capacity.
    for (int i = 0; i < size; i++) {
      referenceMap.put(i, null);
    }
    assertWithMessage("table size after adding %s elements", size)
        .that(initialBuckets)

        // so we rely on the JDK services to eventually look up a custom RootLocator.
        // This is used to compute {@code session.rootDirectory} but all {@code project.rootDirectory}
        // properties will be computed through the RootLocator found in the container.
        RootLocator rootLocator =
                ServiceLoader.load(RootLocator.class).iterator().next();

  }

  public void testInvalidateDuringLoading() throws InterruptedException, ExecutionException {
    // computation starts; invalidate() is called on the key being computed, computation finishes
    CountDownLatch computationStarted = new CountDownLatch(2);
    CountDownLatch letGetFinishSignal = new CountDownLatch(1);
    CountDownLatch getFinishedSignal = new CountDownLatch(2);

  }

  public void testInvalidateDuringLoading() throws InterruptedException, ExecutionException {
    // computation starts; invalidate() is called on the key being computed, computation finishes
    CountDownLatch computationStarted = new CountDownLatch(2);
    CountDownLatch letGetFinishSignal = new CountDownLatch(1);
    CountDownLatch getFinishedSignal = new CountDownLatch(2);

common:cuda_clang --copt=-Qunused-arguments
# Select supported compute capabilities (supported graphics cards).
# This is the same as the official TensorFlow builds.
# See https://developer.nvidia.com/cuda-gpus#compute
# `compute_XY` enables PTX embedding in addition to SASS. PTX
# is forward compatible beyond the current compute capability major
# release while SASS is only forward compatible inside the current

                                    TF_Status* status, TF_Output* dy);

// Adds operations to compute the partial derivatives of sum of `y`s w.r.t `x`s,
// i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...
// This is a variant of TF_AddGradients that allows to caller to pass a custom
// name prefix to the operations added to a graph to compute the gradients.
//
// `dx` are used as initial gradients (which represent the symbolic partial

      }
    }

    @Override
    public @Nullable V compute(
        K key,
        BiFunction<? super K, ? super @Nullable V, ? extends @Nullable V> remappingFunction) {
      synchronized (mutex) {
        return delegate().compute(key, remappingFunction);
      }
    }

 * }
 * }
 *
 * <p>would be flagged as having called {@code sqrt()} with an illegal argument.
 *
 * <h3>Performance</h3>
 *
 * <p>Avoid passing message arguments that are expensive to compute; your code will always compute
 * them, even though they usually won't be needed. If you have such arguments, use the conventional
 * if/throw idiom instead.
 *

 * }
 * }
 *
 * <p>would be flagged as having called {@code sqrt()} with an illegal argument.
 *
 * <h3>Performance</h3>
 *
 * <p>Avoid passing message arguments that are expensive to compute; your code will always compute
 * them, even though they usually won't be needed. If you have such arguments, use the conventional
 * if/throw idiom instead.
 *