code: def predict_step(self, data):
      x, _, _ = data_adapter_utils.unpack_x_y_sample_weight(data)
      if self._call_has_training_arg:
          y_pred = self(x, training=False)
      else:
          y_pred = self(x)
      return y_pred
  
imports: import numpy
  import tree
  from keras import backend
  from keras import callbacks
  from keras.backend.common import standardize_dtype
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.backend.numpy.core import is_tensor
  from keras.trainers import trainer
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.trainers.epoch_iterator import EpochIterator
  from keras.utils import traceback_utils
label_desc: Use image node_id 3 for calling the NumpyTrainer obj's underlying member method code with example usage: obj.predict_step(data) and returns: y_pred