class_docstr: The Classifier Network.
  
  Predicts whether a tuple pair matches or not given a representation of all the
  attribute summarizations.
  `Refer this tutorial <https://nbviewer.jupyter.org/github/sidharthms/deepmatcher/blob/master/examples/matching_models.ipynb#3.-Classifier>`__
  for details.
  
  Args:
      transform_network (string or :class:`~deepmatcher.modules.Transform` or callable):
          The neural network to transform the input vector of the classifier to a
          hidden representation of size `hidden_size`. Argument must specify a
          :ref:`transform-op` operation.
  
      hidden_size (int):
          The size of the hidden representation generated by the transformation network.
          If None, uses the size of the input vector to this module as the hidden size.
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code: def __init__(self, transform_network, hidden_size=None):
      super(Classifier, self).__init__()
      if transform_network:
          self.add_module(
              "transform",
              dm.modules._transform_module(
                  transform_network, hidden_size
              ),
          )
      self.add_module(
          "softmax_transform",
          dm.modules.Transform(
              "1-layer", non_linearity=None, output_size=2
          ),
      )
      self.add_module("softmax", nn.LogSoftmax(dim=1))
  
imports: import copy
  import logging
  from collections import Mapping
  import dill
  import six
  import deepmatcher
  import torch
  import torch.nn
  from .None import _utils
  from ..data import MatchingDataset, MatchingIterator
  from ..runner import Runner
  from ..utils import Bunch, tally_parameters
label_desc: Use image node_id 1 to create a new Classifier object from inherited base classes: nn with example: obj = Classifier(transform_network, hidden_size)