def add_channel(self, name, ipt, val, dataset=None, prereqs=None,
                    data_specs=None):
        """
        Asks the monitor to start tracking a new value.  Can be called
        even after the monitor is already in use.

        Parameters
        ----------
        name : str
            The display name in the monitor.
        ipt : tensor_like
            The symbolic tensor which should be clamped to the data.
            (or a list/tuple containing symbolic tensors, following the
            data_specs)
        val : tensor_like
            The value (function of `ipt`) to be tracked.
        dataset : pylearn2.datasets.Dataset
            Which dataset to compute this channel on
        prereqs : list of callables that take a list of numpy tensors
            Each prereq must be called exactly once per each new batch
            of data drawn *from dataset* before the channel value is
            computed if two channels provide a prereq with exactly the
            same id, that prereq will only be called once
        data_specs : (space, source) pair
            Identifies the order, format and semantics of ipt
        """
        if six.PY3:
            numeric = (float, int)
        else:
            numeric = (float, int, long)  # noqa

        if isinstance(val, numeric):
            val = np.cast[theano.config.floatX](val)

        val = T.as_tensor_variable(val)

        if data_specs is None:
            warnings.warn("parameter 'data_specs' should be provided when " +
                          "calling add_channel. We will build a default one.",
                          stacklevel=2)
            if isinstance(ipt, list):
                ipt = tuple(ipt)
            if ipt is not None and not isinstance(ipt, tuple):
                ipt = (ipt,)

            if ipt is None:
                data_specs = (NullSpace(), '')
            elif len(ipt) == 0:
                data_specs = (CompositeSpace([]), ())
            elif hasattr(dataset, 'get_data_specs'):
                dataset_space, dataset_source = dataset.get_data_specs()
                if (len(ipt) == 1 and
                        dataset_source is not None and
                        (not isinstance(dataset_source, tuple) or
                            len(dataset_source) == 1) and
                        'features' in dataset_source):
                    data_specs = (dataset_space, dataset_source)
                elif (len(ipt) == 2 and
                        dataset_source == ('features', 'targets')):
                    data_specs = (dataset_space, dataset_source)
                else:
                    raise ValueError("Cannot infer default data_specs for " +
                                     "the following input points and " +
                                     "dataset: ipt = %s, dataset = %s"
                                     % (ipt, dataset))

        data_specs[0].validate(ipt)

        mapping = DataSpecsMapping(data_specs)
        flat_ipt = mapping.flatten(ipt)
        if not isinstance(flat_ipt, tuple):
            flat_ipt = (flat_ipt,)
        inputs = theano.gof.graph.inputs([val])
        for elem in inputs:
            if not hasattr(elem, 'get_value') and \
               not isinstance(elem, theano.gof.graph.Constant):
                if elem not in flat_ipt:
                    raise ValueError("Unspecified input: " + str(elem) +
                                     ". This may be due to an incorrect " +
                                     "implementation of a cost's " +
                                     "get_data_specs() method, or of a " +
                                     "model's get_monitoring_data_specs() " +
                                     "method.")

        mode = self.theano_function_mode
        if mode is not None and hasattr(mode, 'record'):
            mode.record.handle_line('Adding monitor channel '+name+'\n')
            assert isinstance(flat_ipt, tuple)
            if len(flat_ipt) != 1:
                for elem in flat_ipt:
                    mode.record.handle_line('Includes input var ' +
                                            var_descriptor(elem) + '\n')
            else:
                mode.record.handle_line(name + ' input var is ' +
                                        var_descriptor(flat_ipt[0]) + '\n')
            mode.record.handle_line('channel ' + name + ' is ' +
                                    var_descriptor(val) + '\n')

        if dataset is None:
            if len(self._datasets) == 1:
                dataset = self._datasets[0]
            elif len(self._datasets) == 0:
                raise ValueError(_err_no_data)
            else:
                raise ValueError(_err_ambig_data)

        try:
            self._datasets.index(dataset)
        except ValueError:
            reraise_as(ValueError("The dataset specified is not one of the " +
                                  "monitor's datasets"))

        if ((self.on_channel_conflict not in
             ('error', 'copy_history', 'overwrite'))):
            raise ValueError("on_channel_conflict should be either 'error'" +
                             "'copy_history', or 'overwrite'")

        if self.channels in name and self.on_channel_conflict == 'error':
            raise ValueError("Tried to create the same channel twice (%s)" %
                             name)
        elif ((name in self.channels and
               self.on_channel_conflict == 'copy_history')):
            self.channels[name] = MonitorChannel(ipt, val, name, data_specs,
                                                 dataset, prereqs,
                                                 self.channels[name])
        elif ((name not in self.channels or
               self.on_channel_conflict == 'overwrite')):
            self.channels[name] = MonitorChannel(ipt, val, name, data_specs,
                                                 dataset, prereqs)
        self._dirty = True
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/monitor.py/Monitor.add_channel/swapped operands of `in`