});
          $boxElement.on("mouseleave", function () {
            isMouseHover = false;
          });

          this.resize();
          suggestor = this;
          $(window).resize(function () {
            suggestor.resize();
          });

          $("body").append($boxElement);
        },

        /**
         * Fetch and display suggestions based on current input

	if !reflect.DeepEqual(lc.Keys(), []string{"key1"}) {
		t.Fatalf("value differs from expected")
	}

	if lc.Resize(0) != 0 {
		t.Fatalf("evicted count differs from expected")
	}
	if lc.Resize(2) != 0 {
		t.Fatalf("evicted count differs from expected")
	}
	lc.Add("key2", "val2")
	if lc.Resize(1) != 1 {
		t.Fatalf("evicted count differs from expected")
	}
}

func TestLRUEdgeCases(t *testing.T) {

  dlm_tensor->dl_tensor.dtype = tf_dlm_type;

  std::vector<int64_t>* shape_arr = &tf_dlm_tensor_ctx->shape;
  std::vector<int64_t>* stride_arr = &tf_dlm_tensor_ctx->strides;
  shape_arr->resize(ndim);
  stride_arr->resize(ndim, 1);
  for (int i = 0; i < ndim; i++) {
    (*shape_arr)[i] = tensor->dim_size(i);
  }
  for (int i = ndim - 2; i >= 0; --i) {
    (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1];

  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();

 * 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

}

// 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.
     *

 * 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