#include "tensorflow/core/util/debug_data_dumper.h"

using tensorflow::errors::InvalidArgument;

namespace tensorflow {
namespace {

Status ValidateNonRefOutput(const Node* node, int idx) {
  const DataType& dt = node->output_type(idx);
  return IsRefType(dt)
             ? InvalidArgument("Output ", idx, " of node '", node->name(),
                               "' has a reference type ", DataTypeString(dt))
             : absl::OkStatus();
}

int TF_OperationNumOutputs(TF_Operation* oper) {
  return oper->node.num_outputs();
}

TF_DataType TF_OperationOutputType(TF_Output oper_out) {
  return static_cast<TF_DataType>(
      oper_out.oper->node.output_type(oper_out.index));
}

int TF_OperationOutputListLength(TF_Operation* oper, const char* arg_name,
                                 TF_Status* status) {
  NameRangeMap name_ranges;
  status->status =

        `tf.keras.mixed_precision.LossScaleOptimizer`

### `tf.lite`:

*   `TFLiteConverter`:
    *   Support optional flags `inference_input_type` and
        `inference_output_type` for full integer quantized models. This allows
        users to modify the model input and output type to integer types
        (`tf.int8`, `tf.uint8`) instead of defaulting to float type
        (`tf.float32`).