input_list.emplace_back(&inputs[i].oper->node, inputs[i].index);
  }
  desc->node_builder.Input(input_list);
}

void TF_AddControlInput(TF_OperationDescription* desc, TF_Operation* input) {
  desc->node_builder.ControlInput(&input->node);
}

void TF_ColocateWith(TF_OperationDescription* desc, TF_Operation* op) {
  desc->colocation_constraints.emplace(

      // We assume that any tensors already placed on this device are
      // ParallelTensors.
      typed_inputs.emplace_back(reinterpret_cast<ParallelTensor*>(
          TFE_TensorHandleDevicePointer(input, status)));
      if (TF_GetCode(status) != TF_OK) return;
    } else {
      typed_inputs.emplace_back(input);
    }
  }

  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Pack 3 variable handles into one TFE_TensorHandle.
  // When remote is false, function device is placed on task0. Handle types are
  // REMOTE, REMOTE, LOCAL on task0. When remote is true, function device is
  // placed on task1, Handle types are LOCAL, REMOTE, LOCAL on task1.
  int num_replicas = 3;
  std::vector<TFE_TensorHandle*> handles = {h0, h1, h2};

                                    "Encountered while processing input ", i,
                                    " into function '", fn_name, "'");

    input_tensors->emplace_back(node, idx);

    const auto& iter = input_nodes->find(node);
    if (iter == input_nodes->end()) {
      input_nodes->insert({node, {idx}});
    } else {
      auto& indices = iter->second;

    return;
  }
  unwrapped_results.resize(real_num_outputs);
  outputs->reserve(real_num_outputs);
  for (TFE_TensorHandle* unwrapped_result : unwrapped_results) {
    outputs->emplace_back(unwrapped_result);
  }
}

ParallelDevice::ParallelDevice(const std::vector<std::string>& devices,
                               bool is_async, int in_flight_nodes_limit)
    : underlying_devices_(devices),

  int num_retvals = 1;
  TFE_Execute(op.get(), &result_handle, &num_retvals, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  return TensorHandlePtr(result_handle);
}

// Create and modify a variable placed on a parallel device which composes
// `first_device` and `second_device`.
void BasicTestsForTwoDevices(TFE_Context* context, const char* first_device,
                             const char* second_device) {

  LoggingDevice* dev = reinterpret_cast<LoggingDevice*>(device_info);
  if (dev->strict_scope_placement && *requested_placement == '\0') {
    TF_SetStatus(s, TF_INTERNAL,
                 "Ops must be placed on the device explicitly, or their inputs "
                 "first copied to other devices.");
    return;
  }
  TFE_Context* context = TFE_OpGetContext(original_op, s);
  if (TF_GetCode(s) != TF_OK) return;

  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  ExpectScalarEq<float>(second_components[1].get(), 9.);

  // Verify that the mirrors are placed on the component devices.
  std::string first_device = TFE_TensorHandleBackingDeviceName(
      second_components[0].get(), status.get());
  ASSERT_EQ(second_underlying_devices[0], first_device);

    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);
    for (int i = 0; i < num_inputs; ++i) {
      if (input_tensors[i] == nullptr) continue;
      all_input_tensors.emplace_back();
      Tensor& input_tensor = all_input_tensors.back();
      status->status = TF_TensorToTensor(input_tensors[i], &input_tensor);
      if (!status->status.ok()) return;
      input_tensors_vector[i] = &input_tensor;

  lru_list_.push_front(key);
  lra_list_.push_front(key);
  new_entry->lru_iterator = lru_list_.begin();
  new_entry->lra_iterator = lra_list_.begin();
  new_entry->timestamp = timer_seconds_();
  block_map_.emplace(std::make_pair(key, new_entry));
  return new_entry;
}

// Remove blocks from the cache until we do not exceed our maximum size.
void RamFileBlockCache::Trim() {
  while (!lru_list_.empty() && cache_size_ > max_bytes_) {