code: def check_data(self, X, y):
      if (
          (y is None)
          and (not is_dataset(X))
          and (self.iterator_train is DataLoader)
      ):
          msg = (
              "No y-values are given (y=None). You must either supply a "
              "Dataset as X or implement your own DataLoader for "
              "training (and your validation) and supply it using the "
              "``iterator_train`` and ``iterator_valid`` parameters "
              "respectively."
          )
          raise ValueError(msg)
  
      if (y is None) and is_dataset(X):
          try:
              _, y_ds = data_from_dataset(X)
              self.classes_inferred_ = np.unique(to_numpy(y_ds))
          except AttributeError:
              # If this fails, we might still be good to go, so don't raise
              pass
  
      if y is not None:
          # pylint: disable=attribute-defined-outside-init
          self.classes_inferred_ = np.unique(to_numpy(y))
  
imports: import re
  import numpy
  from sklearn.base import ClassifierMixin
  import torch
  from torch.utils.data import DataLoader
  from skorch import NeuralNet
  from skorch.callbacks import EpochTimer
  from skorch.callbacks import PrintLog
  from skorch.callbacks import EpochScoring
  from skorch.callbacks import PassthroughScoring
  from skorch.dataset import ValidSplit
  from skorch.utils import data_from_dataset, is_dataset, get_dim, to_numpy
label_desc: Use image node_id 4 for calling the NeuralNetClassifier obj's underlying member method code with example usage: obj.check_data(X, y) without return types