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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 similarity_graph(self, k=5, radius=None, include_self_edges=False, output_type='SGraph', verbose=True):
 """ Construct the similarity graph on the reference dataset, which is already stored in the model to find the top `k` similar images for each image in your input dataset. This is conceptually very similar to running `query` with the reference set, but this method is optimized for the purpose, syntactically simpler, and automatically removes self-edges. WARNING: This method can take time. Parameters k : int, optional Maximum number of neighbors to return for each point in the dataset. Setting this to ``None`` deactivates the constraint, so that all neighbors are returned within ``radius`` of a given point. radius : float, optional For a given point, only neighbors within this distance are returned. The default is ``None``, in which case the ``k`` nearest neighbors are returned for each query point, regardless of distance. include_self_edges : bool, optional For most distance functions, each point in the model's reference dataset is its own nearest neighbor. If this parameter is set to False, this result is ignored, and the nearest neighbors are returned *excluding* the point itself. output_type : {'SGraph', 'SFrame'}, optional By default, the results are returned in the form of an SGraph, where each point in the reference dataset is a vertex and an edge A -> B indicates that vertex B is a nearest neighbor of vertex A. If 'output_type' is set to 'SFrame', the output is in the same form as the results of the 'query' method: an SFrame with columns indicating the query label (in this case the query data is the same as the reference data), reference label, distance between the two points, and the rank of the neighbor. verbose : bool, optional If True, print progress updates and model details. Returns ------- out : SFrame or SGraph The type of the output object depends on the 'output_type' parameter. See the parameter description for more detail. Notes ----- - If both ``k`` and ``radius`` are set to ``None``, each data point is matched to the entire dataset. If the reference dataset has :math:`n` rows, the output is an SFrame with :math:`n^2` rows (or an SGraph with :math:`n^2` edges). Examples -------- | __src_id | __dst_id | distance | rank | | 0 | 1 | 0.376430604494 | 1 | | 2 | 1 | 0.55542776308 | 1 | | 1 | 0 | 0.376430604494 | 1 | """

        return self.similarity_model.similarity_graph(k, radius, include_self_edges, output_type, verbose)