docstr: Perform an inference step
  
  The first output of the ``module`` must be a single array that
  has either shape (n,) or shape (n, 1). In the latter case, the
  output will be reshaped to become 1-dim.
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code: def infer(self, x, **fit_params):
      """Perform an inference step
  
      The first output of the ``module`` must be a single array that
      has either shape (n,) or shape (n, 1). In the latter case, the
      output will be reshaped to become 1-dim.
  
      """
      y_infer = super().infer(x, **fit_params)
      rest = None
      if isinstance(y_infer, tuple):
          y_infer, *rest = y_infer
  
      if (y_infer.dim() > 2) or (
          (y_infer.dim() == 2) and (y_infer.shape[1] != 1)
      ):
          raise ValueError(
              "Expected module output to have shape (n,) or "
              "(n, 1), got {} instead".format(tuple(y_infer.shape))
          )
  
      y_infer = y_infer.reshape(-1)
      if rest is None:
          return y_infer
      return (y_infer,) + tuple(rest)
  
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 5 for calling the NeuralNetBinaryClassifier obj's underlying member method code with example usage: obj.infer(x) and returns: unknown, y_infer