* 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

  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];

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...

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]);
}

PSED)) {\n      this.expand()\n    } else {\n      this.collapse()\n    }\n  }\n\n  autoCollapse(resize = false) {\n    if (!this._options.autoCollapseSize) {\n      return\n    }\n\n    const $bodySelector = $(SELECTOR_BODY)\n\n    if ($(window).width() <= this._options.autoCollapseSize) {\n      if (!$bodySelector.hasClass(CLASS_NAME_OPEN)) {\n        this.collapse()\n      }\n    } else if (resize === true) {\n      if ($bodySelector.hasClass(CLASS_NAME_OPEN)) {\n        $bodySelector.removeC...

update the popper position\n * @method\n * @memberof Popper.Utils\n * @private\n */\nexport default function setupEventListeners(\n  reference,\n  options,\n  state,\n  updateBound\n) {\n  // Resize event listener on window\n  state.updateBound = updateBound;\n  getWindow(reference).addEventListener('resize', state.updateBound, { passive: true });\n\n  // Scroll event listener on scroll parents\n  const scrollElement = getScrollParent(reference);\n  attachToScrollParents(\n    scrollElement,\n  ...

r||M(n(p.parentNode),t,o,i),i.push(p)}function F(e,t,o,i){o.updateBound=i,A(e).addEventListener('resize',o.updateBound,{passive:!0});var r=n(e);return M(r,'scroll',o.updateBound,o.scrollParents),o.scrollElement=r,o.eventsEnabled=!0,o}function I(){this.state.eventsEnabled||(this.state=F(this.reference,this.options,this.state,this.scheduleUpdate))}function R(e,t){return A(e).removeEventListener('resize',t.updateBound),t.scrollParents.forEach(function(e){e.removeEventListener('scroll',t.updateBound...

  }

  TFE_Execute(op_.get(), unwrapped_results.data(), &real_num_outputs, status);
  if (TF_GetCode(status) != TF_OK) {
    cancellation_manager_->StartCancel();
    return;
  }
  unwrapped_results.resize(real_num_outputs);
  outputs->reserve(real_num_outputs);
  for (TFE_TensorHandle* unwrapped_result : unwrapped_results) {
    outputs->emplace_back(unwrapped_result);
  }
}

  DCHECK(tf_results->missing_unused_key_names_data.empty());

  size_t size = results.missing_unused_input_map_keys.size();
  tf_results->missing_unused_key_names.resize(size);
  tf_results->missing_unused_key_indexes.resize(size);

  for (int i = 0; i < size; ++i) {
    TensorId id = results.missing_unused_input_map_keys[i];
    tf_results->missing_unused_key_names_data.emplace_back(id.first);