class_docstr: CIFAR-10 dataset.
class_docstr_tok: ["CIFAR-10","dataset","."]
docstr: Loads data into memory.
docstr_tok: ["Loads","data","into","memory","."]
code: def _load_data(self):
      """Loads data into memory."""
      logger.info(
          "{} data path: {}".format(self._split, self._data_path)
      )
      # Compute data batch names
      if self._split == "train":
          batch_names = ["data_batch_{}".format(i) for i in range(1, 6)]
      else:
          batch_names = ["test_batch"]
      # Load data batches
      inputs, labels = [], []
      for batch_name in batch_names:
          batch_path = os.path.join(self._data_path, batch_name)
          with pathmgr.open(batch_path, "rb") as f:
              data = pickle.load(f, encoding="bytes")
          inputs.append(data[b"data"])
          labels += data[b"labels"]
      # Combine and reshape the inputs
      assert cfg.TRAIN.IM_SIZE == 32, "CIFAR-10 images are 32x32"
      inputs = np.vstack(inputs).astype(np.float32)
      inputs = inputs.reshape(
          (-1, 3, cfg.TRAIN.IM_SIZE, cfg.TRAIN.IM_SIZE)
      )
      return inputs, labels
  
file_path: pycls/pycls/datasets/cifar10.py
imports: import os
  import pickle
  import numpy
  import pycls.core.logging
  import torch.utils.data
  from pycls.core.config import cfg
  from pycls.core.io import pathmgr
label_desc: Use image node_id 2 for calling the Cifar10 obj's underlying member method code with example usage: obj._load_data() and returns: inputs,  labels