code: def _predict_numpy(
      self,
      data: Union[np.ndarray, Dict[str, np.ndarray]],
      dtype: Optional[Union[TensorDtype, Dict[str, TensorDtype]]],
  ) -> Union[np.ndarray, Dict[str, np.ndarray]]:
      # Single column selection return numpy array so preprocessors can be
      # reused in both training and prediction
      if isinstance(data, dict) and len(data) == 1:
          data = next(iter(data.values()))
      model_input = self._arrays_to_tensors(data, dtype)
      model_output = self.call_model(model_input)
      # TODO (jiaodong): Investigate perf implication of this.
      # Move DL Tensor to CPU and convert to numpy.
      if isinstance(model_output, dict):
          return {
              k: self._tensor_to_array(v)
              for k, v in model_output.items()
          }
      else:
          return {"predictions": self._tensor_to_array(model_output)}
  
file_path: ray/python/ray/train/_internal/dl_predictor.py
imports: import abc
  from typing import Dict, Optional, TypeVar, Union
  import numpy
  import pandas
  from ray.air.util.data_batch_conversion import BatchFormat, _convert_batch_type_to_pandas, _convert_pandas_to_batch_type
  from ray.train.predictor import Predictor
  from ray.util.annotations import DeveloperAPI
label_desc: Use image node_id 6 for calling the DLPredictor obj's underlying member method code with example usage: obj._predict_numpy(data, dtype) and returns: unknown, dict