@staticmethod<EOL><INDENT>def from_dict(input_dict, data=None):<DEDENT>
import GPy<EOL>m = GPy.core.model.Model.from_dict(input_dict, data)<EOL>from copy import deepcopy<EOL>sparse_gp = deepcopy(m)<EOL>return SparseGPClassification(sparse_gp.X, sparse_gp.Y, sparse_gp.Z, sparse_gp.kern, sparse_gp.likelihood,  sparse_gp.inference_method, sparse_gp.mean_function, name='<STR_LIT>')<EOL>
Instantiate an SparseGPClassification object using the information
in input_dict (built by the to_dict method).

:param data: It is used to provide X and Y for the case when the model
   was saved using save_data=False in to_dict method.
:type data: tuple(:class:`np.ndarray`, :class:`np.ndarray`)