outputs_.clear();
  for (TF_Operation* o : outputs) {
    outputs_.emplace_back(TF_Output{o, 0});
  }
  output_values_.resize(outputs_.size());
}

void CSession::SetOutputs(const std::vector<TF_Output>& outputs) {
  ResetOutputValues();
  outputs_ = outputs;
  output_values_.resize(outputs_.size());
}

void CSession::SetTargets(std::initializer_list<TF_Operation*> targets) {
  targets_.clear();

)

if NOT "%FESS_HEAP_SIZE%" == "" (
set FESS_MIN_MEM=%FESS_HEAP_SIZE%
set FESS_MAX_MEM=%FESS_HEAP_SIZE%
)

REM min and max heap sizes should be set to the same value to avoid
REM stop-the-world GC pauses during resize, and so that we can lock the
REM heap in memory on startup to prevent any of it from being swapped
REM out.
set FESS_JAVA_OPTS=%FESS_JAVA_OPTS% -Xms%FESS_MIN_MEM% -Xmx%FESS_MAX_MEM%

 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>Unlike {@code java.util.HashSet}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger

 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>Unlike {@code java.util.HashSet}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger

 * function doing a good job of distributing the elements to the buckets to a distribution not far
 * from uniform), and amortized since some operations can trigger a hash table resize.
 *
 * <p>As compared with {@link java.util.LinkedHashMap}, this structure places significantly reduced
 * load on the garbage collector by only using a constant number of internal objects.
 *

}

// Len returns the number of items in the cache.
func (c *LRU[K, V]) Len() int {
	c.mu.RLock()
	defer c.mu.RUnlock()
	return c.evictList.Length()
}

// Resize changes the cache size. Size of 0 means unlimited.
func (c *LRU[K, V]) Resize(size int) (evicted int) {
	c.mu.Lock()
	defer c.mu.Unlock()
	if size <= 0 {
		c.size = 0
		return 0
	}
	diff := c.evictList.Length() - size
	if diff < 0 {

ion(){var e=this,t=n.default("body");t.hasClass(L)||t.hasClass(D)?(n.default(k).not(this._config.target).hide(),n.default(this._config.target).css("display","block")):n.default(k).hide(),this._fixHeight(),this._fixScrollHeight(),n.default(window).resize((function(){e._fixHeight(),e._fixScrollHeight()})),n.default(window).scroll((function(){var t=n.default("body");(t.hasClass(L)||t.hasClass(D))&&e._fixScrollHeight()}))},t._isNavbarFixed=function(){var e=n.default("body");return e.hasClass("layout...

      // Always presize to at least 1, since we only bother creating a value collection if there's
      // at least one element.
      this.expectedValuesPerKey = max(expectedValuesPerKey, 1);

      return this;
    }

    /**
     * By default, if we are handed a value collection bigger than expectedValuesPerKey, presize to
     * accept that many elements.
     *

void TF_OutputListSetNumOutputs(TF_OutputList* o, int num_outputs,
                                TF_Status* s) {
  unwrap(o)->expected_num_outputs = num_outputs;
  unwrap(o)->outputs.clear();
  unwrap(o)->outputs.resize(num_outputs);
}
int TF_OutputListNumOutputs(TF_OutputList* o) {
  return unwrap(o)->outputs.size();
}
TF_AbstractTensor* TF_OutputListGet(TF_OutputList* o, int i) {
  return wrap(unwrap(o)->outputs[i]);
}

 * function doing a good job of distributing the elements to the buckets to a distribution not far
 * from uniform), and amortized since some operations can trigger a hash table resize.
 *
 * <p>Unlike {@code java.util.HashMap}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger