TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) {
  auto* params =
      reinterpret_cast<TfLiteDepthToSpaceParams*>(node->builtin_data);

  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);

  const TfLiteTensor* input;
  TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input));
  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context,
                    GetOutputSafe(context, node, kOutputTensor, &output));

  TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);

  auto data_type = output->type;
  TF_LITE_ENSURE(context,
                 data_type == kTfLiteFloat32 || data_type == kTfLiteInt8);
  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);

  const int block_size = params->block_size;
  TF_LITE_ENSURE(context, block_size > 0);
  const int input_height = input->dims->data[kHeightRank];
  const int input_width = input->dims->data[kWidthRank];
  const int input_channels = input->dims->data[kDepthRank];
  int output_height = input_height * block_size;
  int output_width = input_width * block_size;
  int output_channels = input_channels / block_size / block_size;

  TF_LITE_ENSURE_EQ(context, input_height, output_height / block_size);
  TF_LITE_ENSURE_EQ(context, input_width, output_width / block_size);
  TF_LITE_ENSURE_EQ(context, input_channels,
                    output_channels * block_size * block_size);

  // We must update the output tensor dimensions.
  // The dims storage is expected to be the same area in memory
  // for both TfLiteTensor and TfLiteEvalTensor.  This is important
  // because TfLiteTensor in the MicroInterpreter is a temporary
  // allocation.  For the KernelRunner interpreter, TfLiteEvalTensor
  // is a temporary allocation.  We must therefore relocate the dims
  // from the FlatBuffer to the persistant storage arena.
  TfLiteEvalTensor* output_eval =
      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
  TF_LITE_ENSURE_OK(context, tflite::micro::CreateWritableTensorDimsWithCopy(
                                 context, output, output_eval));
  output->dims->data[kBatchRank] = input->dims->data[kBatchRank];
  output->dims->data[kHeightRank] = output_height;
  output->dims->data[kWidthRank] = output_width;
  output->dims->data[kDepthRank] = output_channels;

  return kTfLiteOk;
}
TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) {
  auto* params =
      reinterpret_cast<TfLiteDepthToSpaceParams*>(node->builtin_data);

  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);

  const TfLiteTensor* input;
  TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input));
  TfLiteTensor* output;
  TF_LITE_ENSURE_OK(context,
                    GetOutputSafe(context, node, kOutputTensor, &output));

  TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);

  auto data_type = output->type;
  TF_LITE_ENSURE(context,
                 data_type == kTfLiteFloat32 || data_type == kTfLiteInt8);
  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);

  const int block_size = params->block_size;
  TF_LITE_ENSURE(context, block_size > 0);
  const int input_height = input->dims->data[kHeightRank];
  const int input_width = input->dims->data[kWidthRank];
  const int input_channels = input->dims->data[kDepthRank];
  int output_height = input_height * block_size;
  int output_width = input_width * block_size;
  int output_channels = input_channels / block_size / block_size;

  TF_LITE_ENSURE_EQ(context, input_height, output_height / block_size);
  TF_LITE_ENSURE_EQ(context, input_width, output_width / block_size);
  TF_LITE_ENSURE_EQ(context, input_channels,
                    output_channels * block_size * block_size);

   
   
   
   
   
   
   
  TfLiteEvalTensor* output_eval =
      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
  TF_LITE_ENSURE_OK(context, tflite::micro::CreateWritableTensorDimsWithCopy(
                                 context, output, output_eval));
  output->dims->data[kBatchRank] = input->dims->data[kBatchRank];
  output->dims->data[kHeightRank] = output_height;
  output->dims->data[kWidthRank] = output_width;
  output->dims->data[kDepthRank] = output_channels;

  return kTfLiteOk;
}