docstr: Where applicable, return class labels for samples in X.
  
  If the module's forward method returns multiple outputs as a
  tuple, it is assumed that the first output contains the
  relevant information and the other values are ignored. If all
  values are relevant, consider using
  :func:`~skorch.NeuralNet.forward` instead.
  
  Parameters
  ----------
  X : input data, compatible with skorch.dataset.Dataset
    By default, you should be able to pass:
  
      * numpy arrays
      * torch tensors
      * pandas DataFrame or Series
      * scipy sparse CSR matrices
      * a dictionary of the former three
      * a list/tuple of the former three
      * a Dataset
  
    If this doesn't work with your data, you have to pass a
    ``Dataset`` that can deal with the data.
  
  Returns
  -------
  y_pred : numpy ndarray
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code: def predict(self, X):
      """Where applicable, return class labels for samples in X.
  
      If the module's forward method returns multiple outputs as a
      tuple, it is assumed that the first output contains the
      relevant information and the other values are ignored. If all
      values are relevant, consider using
      :func:`~skorch.NeuralNet.forward` instead.
  
      Parameters
      ----------
      X : input data, compatible with skorch.dataset.Dataset
        By default, you should be able to pass:
  
          * numpy arrays
          * torch tensors
          * pandas DataFrame or Series
          * scipy sparse CSR matrices
          * a dictionary of the former three
          * a list/tuple of the former three
          * a Dataset
  
        If this doesn't work with your data, you have to pass a
        ``Dataset`` that can deal with the data.
  
      Returns
      -------
      y_pred : numpy ndarray
  
      """
      return self.predict_proba(X).argmax(axis=1)
  
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 8 for calling the NeuralNetClassifier obj's underlying member method code with example usage: obj.predict(X) and returns: self