}
            );

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

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

          suggest: function() {
            suggestingSts = true;

            this.resize();

            var suggestor = this;

      newDelegate.addAll(delegate);
      this.table = newDelegate;
      return;
    }
    int size = this.size;
    if (size < requireEntries().length) {
      resizeEntries(size);
    }
    int minimumTableSize = CompactHashing.tableSize(size);
    int mask = hashTableMask();
    if (minimumTableSize < mask) { // smaller table size will always be less than current mask

      newDelegate.addAll(delegate);
      this.table = newDelegate;
      return;
    }
    int size = this.size;
    if (size < requireEntries().length) {
      resizeEntries(size);
    }
    int minimumTableSize = CompactHashing.tableSize(size);
    int mask = hashTableMask();
    if (minimumTableSize < mask) { // smaller table size will always be less than current mask

set FESS_MIN_MEM=256m
)

if "%FESS_MAX_MEM%" == "" (
set FESS_MAX_MEM=1g
)

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.

    }
    int size = this.size;
    if (size < requireEntries().length) {
      resizeEntries(size);
    }
    int minimumTableSize = CompactHashing.tableSize(size);
    int mask = hashTableMask();
    if (minimumTableSize < mask) { // smaller table size will always be less than current mask
      resizeTable(mask, minimumTableSize, UNSET, UNSET);
    }
  }

  @Override
  public void clear() {

  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];
  }

      std::vector<AbstractTensorHandle*> func_inputs;
      func_inputs.reserve(inputs.size());
      TF_RETURN_IF_ERROR(
          CreateParamsForInputs(func_ctx.get(), inputs, &func_inputs));
      std::vector<AbstractTensorHandle*> model_outputs;
      model_outputs.resize(outputs.size());
      TF_RETURN_IF_ERROR(model(func_ctx.get(), absl::MakeSpan(func_inputs),

 * 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.
 *

 * 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.
 *

    int size = this.size;
    if (size < requireEntries().length) {
      resizeEntries(size);
    }
    int minimumTableSize = CompactHashing.tableSize(size);
    int mask = hashTableMask();
    if (minimumTableSize < mask) { // smaller table size will always be less than current mask
      resizeTable(mask, minimumTableSize, UNSET, UNSET);
    }
  }

  @Override
  public void clear() {
    if (needsAllocArrays()) {