TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
  OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
  int num_inputs = node->inputs->size;
  // The number of outputs should be the same as number of inputs.
  TF_LITE_ENSURE_EQ(context, node->outputs->size, num_inputs);

  // Check subgraph indices and get subgraphs.
  Subgraph* this_subgraph = reinterpret_cast<Subgraph*>(context->impl_);
  auto* subgraphs = this_subgraph->GetSubgraphs();
  TF_LITE_ENSURE(context, op_data->cond_subgraph_index < subgraphs->size());
  TF_LITE_ENSURE(context, op_data->body_subgraph_index < subgraphs->size());

  Subgraph* cond_subgraph = (*subgraphs)[op_data->cond_subgraph_index].get();
  Subgraph* body_subgraph = (*subgraphs)[op_data->body_subgraph_index].get();

  // Check input & output count of the condition subgraph.
  TF_LITE_ENSURE_EQ(context, cond_subgraph->inputs().size(), num_inputs);
  TF_LITE_ENSURE_EQ(context, cond_subgraph->outputs().size(), 1);

  // Check input & output count of the body subgraph.
  TF_LITE_ENSURE_EQ(context, body_subgraph->inputs().size(), num_inputs);
  TF_LITE_ENSURE_EQ(context, body_subgraph->outputs().size(), num_inputs);

  // Prepare and check the condition subgraph.
  TF_LITE_ENSURE_OK(
      context, CopyTensorsShapeAndType(
                   context, this_subgraph, TfLiteIntArrayView(node->inputs),
                   cond_subgraph, cond_subgraph->inputs(), true));
  TF_LITE_ENSURE_OK(context, cond_subgraph->AllocateTensors());
  TfLiteTensor* cond_output =
      cond_subgraph->tensor(cond_subgraph->outputs()[0]);
  // TODO(ycling): Handle the case the cond subgraph has dynamic tensor outputs.
  // This should rarely happens. In most cases the output is static with shape
  // [1]. However theoretically intermediate tensors in the cond subgraph
  // can be dynamic.
  if (IsDynamicTensor(cond_output)) {
    op_data->cond_has_dynamic_output_tensors = true;
  } else {
    TF_LITE_ENSURE_STATUS(CheckCondOutput(context, cond_output));
  }

  // Prepare and check the body subgraph.
  TF_LITE_ENSURE_OK(
      context, CopyTensorsShapeAndType(
                   context, this_subgraph, TfLiteIntArrayView(node->inputs),
                   body_subgraph, body_subgraph->inputs(), true));
  TF_LITE_ENSURE_OK(context, body_subgraph->AllocateTensors());
  if (body_subgraph->HasDynamicTensors()) {
    op_data->body_has_dynamic_output_tensors = true;
  } else {
    for (int i = 0; i < num_inputs; ++i) {
      TfLiteTensor* body_input =
          body_subgraph->tensor(body_subgraph->inputs()[i]);
      TfLiteTensor* body_output =
          body_subgraph->tensor(body_subgraph->outputs()[i]);
      TF_LITE_ENSURE_TYPES_EQ(context, body_input->type, body_output->type);

      // TODO(ycling): Support dynamic sized body subgraph.
      TF_LITE_ENSURE(context, !IsDynamicTensor(body_output));
      if (!TfLiteIntArrayEqual(body_input->dims, body_output->dims)) {
        // If the output shape of the body subgraph is static w.r.t. a fixed
        // input size, but it's different from input size, it's still considered
        // dynamic. For example: If a subgraph keeps padding its input with a
        // fixed padding, the output shape is static w.r.t the input shape and
        // padding, but running it in a loop will keep bloating the tensor.
        op_data->body_has_dynamic_output_tensors = true;
        break;
      }
    }
  }
  for (int i = 0; i < num_inputs; ++i) {
    TfLiteTensor* output = GetOutput(context, node, i);
    if (op_data->body_has_dynamic_output_tensors) {
      SetTensorToDynamic(output);
    } else {
      TfLiteTensor* body_output =
          body_subgraph->tensor(body_subgraph->outputs()[i]);
      TfLiteIntArray* output_size = TfLiteIntArrayCopy(body_output->dims);
      TF_LITE_ENSURE_OK(context,
                        context->ResizeTensor(context, output, output_size));
    }
  }
  return kTfLiteOk;
}