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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def concatenate(cls, datasets, datatype=None, new_type=None):
 """ Utility function to concatenate an NdMapping of Dataset objects. """

        from . import Dataset, default_datatype
        new_type = new_type or Dataset
        if isinstance(datasets, NdMapping):
            dimensions = datasets.kdims
            keys, datasets = zip(*datasets.data.items())
        elif isinstance(datasets, list) and all(not isinstance(v, tuple) for v in datasets):
            # Allow concatenating list of datasets (by declaring no dimensions and keys)
            dimensions, keys = [], [()]*len(datasets)
        else:
            raise DataError('Concatenation only supported for NdMappings '
                            'and lists of Datasets, found %s.' % type(datasets).__name__)

        template = datasets[0]
        datatype = datatype or template.interface.datatype

        # Handle non-general datatypes by casting to general type
        if datatype == 'array':
            datatype = default_datatype
        elif datatype == 'image':
            datatype = 'grid'

        if len(datasets) > 1 and not dimensions and cls.interfaces[datatype].gridded:
            raise DataError('Datasets with %s datatype cannot be concatenated '
                            'without defining the dimensions to concatenate along. '
                            'Ensure you pass in a NdMapping (e.g. a HoloMap) '
                            'of Dataset types, not a list.' % datatype)

        datasets = template.interface.cast(datasets, datatype)
        template = datasets[0]
        data = list(zip(keys, datasets)) if keys else datasets
        concat_data = template.interface.concat(data, dimensions, vdims=template.vdims)
        return template.clone(concat_data, kdims=dimensions+template.kdims, new_type=new_type)