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());
}

  std::vector<const char*> raw_strs;
  std::vector<size_t> lengths;
  raw_strs.reserve(values.size());
  lengths.reserve(values.size());
  for (const auto& s : values) {
    raw_strs.emplace_back(s.data());
    lengths.emplace_back(s.size());
  }
  return SetAttrStringList(attr_name,
                           reinterpret_cast<const void**>(raw_strs.data()),
                           lengths.data(), values.size());
}

        return true;
      }
    }
  }
  return false;
}

template <typename Gradient, typename BackwardFunction, typename TapeTensor>
void GradientTape<Gradient, BackwardFunction, TapeTensor>::Watch(
    int64_t tensor_id) {
  tensor_tape_.emplace(tensor_id, -1);
}

template <typename Gradient, typename BackwardFunction, typename TapeTensor>

      for (int i = 0; i < num_values; ++i) {
        op_->colocation_constraints.emplace(static_cast<const char*>(values[i]),
                                            lengths[i]);
      }
    } else {
      std::vector<tensorflow::StringPiece> v;
      v.reserve(num_values);
      for (int i = 0; i < num_values; ++i) {
        v.emplace_back(static_cast<const char*>(values[i]), lengths[i]);
      }

    PartialTensorShape shape;
    TF_RETURN_IF_ERROR(input->Shape(&shape));
    TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(ctx)->AddParameter(
        input->DataType(), shape, &handle));
    params->emplace_back(handle);
  }
  return absl::OkStatus();
}

// Runs `model` maybe wrapped in a function.
Status RunModel(Model model, AbstractContext* ctx,
                absl::Span<AbstractTensorHandle* const> inputs,

    for (int i = 0; i < t->num_tensors(); ++i) {
      TensorHandlePtr this_output(
          TFE_TensorHandleCopySharingTensor(t->tensor(i), status));
      outputs.emplace_back(std::move(this_output));
      if (TF_GetCode(status) != TF_OK) return result;
    }
    result.emplace(std::move(outputs));
    return result;
  }
  std::vector<ParallelTensor*> parallel_inputs;

  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_) {

    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(

    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),

      block_size, 2 * callers * block_size, 0, fetcher);
  std::vector<std::unique_ptr<Thread>> threads;
  threads.reserve(callers);
  for (int i = 0; i < callers; i++) {
    threads.emplace_back(
        Env::Default()->StartThread({}, "caller", [block_size, &cache, i]() {
          std::vector<char> out;
          TF_EXPECT_OK(
              ReadCache(&cache, "a", i * block_size, block_size, &out));