TF_Operation* const3;
    if (grad_inputs_provided) {
      const float const3_val[] = {1.0, 1.0, 1.0, 1.0};
      const3 = FloatConst2x2(expected_graph_, s_, const3_val, "GradInputs");
    } else {
      const3 = OnesLike(expected_graph_, s_, matmul, "gradients/OnesLike");
    }

    TF_Operation* matmul1 = MatMul(expected_graph_, s_, const3, const1,

after touch\nconst SWIPE_THRESHOLD = 40\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_RIGHT = 'carousel-item-right'\nconst CLASS_NAME_LEFT = 'carousel-item-left'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\n\nconst DIRECTION_NEXT = 'next'\nconst DIRECTION_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT...

====================================================\n */\n\nconst NAME = 'CardRefresh'\nconst DATA_KEY = 'lte.cardrefresh'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst JQUERY_NO_CONFLICT = $.fn[NAME]\n\nconst EVENT_LOADED = `loaded${EVENT_KEY}`\nconst EVENT_OVERLAY_ADDED = `overlay.added${EVENT_KEY}`\nconst EVENT_OVERLAY_REMOVED = `overlay.removed${EVENT_KEY}`\n\nconst CLASS_NAME_CARD = 'card'\n\nconst SELECTOR_CARD = `.${CLASS_NAME_CARD}`\nconst SELECTOR_DATA_REFRESH = '[data-card-widget=\"card-refresh\"]'\n\nconst...

getOffsetRectRelativeToArbitraryNode(children, parent, fixedPosition = false) {\n  const isIE10 = runIsIE(10);\n  const isHTML = parent.nodeName === 'HTML';\n  const childrenRect = getBoundingClientRect(children);\n  const parentRect = getBoundingClientRect(parent);\n  const scrollParent = getScrollParent(children);\n\n  const styles = getStyleComputedProperty(parent);\n  const borderTopWidth = parseFloat(styles.borderTopWidth);\n  const borderLeftWidth = parseFloat(styles.borderLeftWidth);\n\n  // In cases...

  virtual absl::Status SetAttrInt(const char* attr_name, int64_t value) = 0;
  virtual absl::Status SetAttrFloat(const char* attr_name, float value) = 0;
  virtual absl::Status SetAttrBool(const char* attr_name, bool value) = 0;
  virtual absl::Status SetAttrType(const char* attr_name, DataType value) = 0;
  virtual absl::Status SetAttrShape(const char* attr_name, const int64_t* dims,
                                    const int num_dims) = 0;

    const TF_Operation* const* opers, int ninputs, const TF_Output* inputs,
    int noutputs, const TF_Output* outputs, const char* const* output_names,
    int ncontrol_outputs, const TF_Operation* const* control_outputs,
    const char* const* control_output_names, const TF_FunctionOptions* opts,
    const char* description, TF_Status* status) {
  tensorflow::mutex_lock l(fn_body->mu);

  // Process inputs.

    TFE_MonitoringStringGauge2* gauge, const char* label1, const char* label2) {
  return static_cast<TFE_MonitoringStringGaugeCell*>(
      static_cast<void*>(gauge->gauge->GetCell(label1, label2)));
}

TFE_MonitoringStringGauge3* TFE_MonitoringNewStringGauge3(
    const char* name, TF_Status* status, const char* description,
    const char* label1, const char* label2, const char* label3) {

                                            TF_Status* status) const;

  // The number of devices operations run on.
  size_t num_underlying_devices() const { return underlying_devices_.size(); }

  // The devices operations run on.
  const std::vector<std::string>& underlying_devices() const {
    return underlying_devices_;
  }

  // Takes a description of a single operation being executed on the

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>
ParallelDevice::Execute(TFE_Context* context,
                        const std::vector<ParallelTensor*>& inputs,
                        const char* operation_name,
                        const TFE_OpAttrs* attributes, int expected_max_outputs,
                        TF_Status* status) const {
  std::vector<PartialTensorShape> expected_output_shapes(expected_max_outputs);

                       GradientFunction* gradient_function,
                       const string& op_name = "");
  // Returns whether any tensor in a list of tensors is being watched and has
  // a trainable dtype.
  bool ShouldRecord(
      absl::Span<const AbstractTensorHandle* const> tensors) const;
  // Unwatches this tensor on the tape. Mainly used for cleanup when deleting
  // eager tensors.