def train(self, fold=None, scene_label=None, event_label=None, filename_contains=None, **kwargs):
    'List of training items.\n\n        Parameters\n        ----------\n        fold : int\n            Fold id, if None all meta data is returned.\n            Default value "None"\n        scene_label : str\n            Scene label\n            Default value "None"\n        event_label : str\n            Event label\n            Default value "None"\n        filename_contains : str:\n            String found in filename\n             Default value "None"\n\n        Returns\n        -------\n        list : list of dicts\n            List containing all meta data assigned to training set for given fold.\n\n        '
    if ((fold is None) or (fold == 0)):
        fold = 'all_data'
    data = self.crossvalidation_data['train'][fold]
    if scene_label:
        data = data.filter(scene_label=scene_label)
    if event_label:
        data = data.filter(event_label=event_label)
    if filename_contains:
        data_ = MetaDataContainer()
        for item in data:
            if (filename_contains in item.filename):
                data_.append(item)
        data = data_
    return data
List of training items.

Parameters
----------
fold : int
    Fold id, if None all meta data is returned.
    Default value "None"
scene_label : str
    Scene label
    Default value "None"
event_label : str
    Event label
    Default value "None"
filename_contains : str:
    String found in filename
     Default value "None"

Returns
-------
list : list of dicts
    List containing all meta data assigned to training set for given fold.
def train(self, fold=None, scene_label=None, event_label=None, filename_contains=None, **kwargs):
    'List of training items.\n\n        Parameters\n        ----------\n        fold : int\n            Fold id, if None all meta data is returned.\n            Default value "None"\n        scene_label : str\n            Scene label\n            Default value "None"\n        event_label : str\n            Event label\n            Default value "None"\n        filename_contains : str:\n            String found in filename\n             Default value "None"\n\n        Returns\n        -------\n        list : list of dicts\n            List containing all meta data assigned to training set for given fold.\n\n        '
    if ((fold is None) or (fold == 0)):
        fold = 'all_data'
    data = self.crossvalidation_data['train'][fold]
    if scene_label:
        data = data.filter(scene_label=scene_label)
    if event_label:
        data = data.filter(event_label=event_label)
    if filename_contains:
        data_ = MetaDataContainer()
        for item in data:
            if (filename_contains in item.filename):
                data_.append(item)
        data = data_
    return data