class_docstr: Compute **true** classification error in batch mode, from a ``wrong`` tensor.
  
  The ``wrong`` tensor is supposed to be an binary vector containing
  whether each sample in the batch is *incorrectly* classified.
  You can use ``tf.nn.in_top_k`` to produce this vector.
  
  This Inferencer produces the "true" error, which could be different from
  ``ScalarStats('error_rate')``.
  It takes account of the fact that batches might not have the same size in
  testing (because the size of test set might not be a multiple of batch size).
  Therefore the result can be different from averaging the error rate of each batch.
  
  You can also use the "correct prediction" tensor, then this inferencer will
  give you "classification accuracy" instead of error.
code: def _on_fetches(self, outputs):
      vec = outputs[0]
      # TODO put shape assertion into inference-runner
      assert vec.ndim == 1, "{} is not a vector!".format(
          self.wrong_tensor_name
      )
      batch_size = len(vec)
      wrong = np.sum(vec)
      self.err_stat.feed(wrong, batch_size)
  
imports: import numpy
  from abc import ABCMeta
  import six
  from ..tfutils.common import get_op_tensor_name
  from ..utils import logger
  from ..utils.stats import BinaryStatistics, RatioCounter
  from .base import Callback
label_desc: Use image node_id 4 for calling the ClassificationError obj's underlying member method code with example usage: obj._on_fetches(outputs) without return types