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,

static _jQueryInterface(option, config) {\n    return this.each(function () {\n      const _options = $.extend({}, Default, config)\n      const toast = new Toasts($(this), _options)\n\n      if (option === 'create') {\n        toast[option]()\n      }\n    })\n  }\n}\n\n/**\n * jQuery API\n * ====================================================\n */\n\n$.fn[NAME] = Toasts._jQueryInterface\n$.fn[NAME].Constructor = Toasts\n$.fn[NAME].noConflict = function () {\n  $.fn[NAME] = JQUERY_NO_CONFLICT\n  return...

  companion object {
    val DNS_MESSAGE: MediaType = "application/dns-message".toMediaType()
    const val MAX_RESPONSE_SIZE = 64 * 1024

    private fun buildBootstrapClient(builder: Builder): Dns {
      val hosts = builder.bootstrapDnsHosts

      return if (hosts != null) {
        BootstrapDns(builder.url!!.host, hosts)
      } else {
        builder.systemDns
      }
    }

				diskMap[k] = struct{}{}
			}
		}
	}
	jobID := r.Form.Get("by-jobID")

	hosts := strings.Split(r.Form.Get("hosts"), ",")
	byHost := strings.EqualFold(r.Form.Get("by-host"), "true")
	var hostMap map[string]struct{}
	if len(hosts) > 0 && hosts[0] != "" {
		hostMap = make(map[string]struct{}, len(hosts))
		for _, k := range hosts {
			if k != "" {
				hostMap[k] = struct{}{}
			}
		}
	}

  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) {

				this.mem.setUint32(addr, id, true);
			}

			const loadSlice = (addr) => {
				const array = getInt64(addr + 0);
				const len = getInt64(addr + 8);
				return new Uint8Array(this._inst.exports.mem.buffer, array, len);
			}

			const loadSliceOfValues = (addr) => {
				const array = getInt64(addr + 0);
				const len = getInt64(addr + 8);
				const a = new Array(len);
				for (let i = 0; i < len; i++) {

                                            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);