inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  absl::Status Finalize(OutputList* outputs, AbstractFunction** f) override {
    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {
      GraphTensor* output = dyn_cast<GraphTensor>(abstract_output);

                            int noutputs, const TF_Output* outputs,
                            std::vector<OutputTensor>* output_tensors)
    TF_EXCLUSIVE_LOCKS_REQUIRED(fn_body->mu) {
  output_tensors->reserve(noutputs);
  for (int i = 0; i < noutputs; ++i) {
    Node* node = outputs[i].oper ? &outputs[i].oper->node : nullptr;
    int idx = outputs[i].index;
    TF_RETURN_WITH_CONTEXT_IF_ERROR(

  VLOG(1) << "Running the dequeue op";
  TF_Output output{dequeue_op, 0};
  TF_Tensor* ret;
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,
                // output related parameters
                /*outputs*/ &output, /*output_values*/ &ret,
                /*noutputs*/ 1,

  inputs_.clear();
  for (const auto& p : inputs) {
    inputs_.emplace_back(TF_Output{p.first, 0});
    input_values_.emplace_back(p.second);
  }
}

void CSession::SetOutputs(std::initializer_list<TF_Operation*> outputs) {
  ResetOutputValues();
  outputs_.clear();
  for (TF_Operation* o : outputs) {
    outputs_.emplace_back(TF_Output{o, 0});
  }
  output_values_.resize(outputs_.size());
}

  TF_OutputListSetNumOutputs(mm_outputs, 1, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_ExecuteOperation(fn_op, 1, &input_t, mm_outputs, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  ASSERT_EQ(1, TF_OutputListNumOutputs(mm_outputs));
  TF_AbstractTensor* final_result = TF_OutputListGet(mm_outputs, 0);
  TFE_TensorHandle* final =

  TF_Operation* neg = Neg(feed, func_graph.get(), s.get());

  TF_Output inputs[] = {{feed, 0}};
  TF_Output outputs[] = {{neg, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name, append_hash, -1,
                             /*opers=*/nullptr, 1, inputs, 1, outputs,
                             /*output_names=*/nullptr,
                             /*opts=*/nullptr, description, s.get());

// 'retvals' must point to a pre-allocated array of TFE_TensorHandle* and
// '*num_retvals' should be set to the size of this array. It is an error if
// the size of 'retvals' is less than the number of outputs. This call sets
// *num_retvals to the number of outputs.
//
// If async execution is enabled, the call may simply enqueue the execution
// and return "non-ready" handles in `retvals`. Note that any handles contained

        }
    }

    /**
     * Outputs INFO information.
     *
     * @param message
     *            Message
     */
    public void info(final Object message) {
        if (isInfoEnabled()) {
            log.info(toString(message));
        }
    }

    /**
     * Outputs WARN information.
     *
     * @param message
     *            Message

        absl::StrCat("ZerosLike", ToId(t)).c_str()));
  }
  TF_RETURN_IF_ERROR(op->AddInput(t));
  int num_outputs = 1;
  std::vector<AbstractTensorHandle*> outputs(num_outputs);
  TF_RETURN_IF_ERROR(
      op->Execute(absl::Span<AbstractTensorHandle*>(outputs), &num_outputs));
  *result = outputs[0];
  return absl::OkStatus();
}
}  // namespace

absl::Status GradientRegistry::Register(

    }
  }

  sourceSets {
    val commonJvmAndroid =
      create("commonJvmAndroid") {
        dependsOn(commonMain.get())

        kotlin.srcDir(copyKotlinTemplates.map { it.outputs })
        kotlin.srcDir(generateIdnaMappingTable.map { it.outputs })

        dependencies {
          api(libs.square.okio)
          api(libs.kotlin.stdlib)

          compileOnly(libs.animalsniffer.annotations)
        }
      }