How to use the hypernetx.exception.HyperNetXError function in hypernetx

M = np.array(M)
		if len(M.shape) != (2):
			raise HyperNetXError('Input requires a 2 dimensional numpy array')

		if node_names is not None:
		    nodenames = np.array(node_names)
		    if len(nodenames) != M.shape[0]:
		        raise HyperNetXError('Number of node names does not match number of rows.')
		else:
		    nodenames = np.array([f'v{idx}' for idx in range(M.shape[0])])

		if edge_names is not None:
		    edgenames = np.array(edge_names)
		    if len(edgenames) != M.shape[1]:
		    	raise HyperNetXError('Number of edge_names does not match number of columns.')
		else:
		    edgenames = np.array([f'e{jdx}' for jdx in range(M.shape[1])])

		## apply boolean key if available
		if key:
			M = key(M)

		## Remove empty column indices from M columns and edgenames
		colidx = np.array([jdx for jdx in range(M.shape[1]) if any(M[:,jdx])])
		colidxsum = np.sum(colidx)
		if not colidxsum:
			return Hypergraph()
		else:
			M = M[:,colidx]
			edgenames = edgenames[colidx]
			edict = dict()

If hypergraph is not s-edge-connected

		Notes
		-----
		Two nodes are s-adjacent if they share s edges.
		Two nodes v_start and v_end are s-walk connected if there is a sequence of
		nodes v_start, v_1, v_2, ... v_n-1, v_end such that consecutive nodes
		are s-adjacent. If the graph is not connected, an error will be raised.

		"""
		A = self.adjacency_matrix(s=s)
		G = nx.from_scipy_sparse_matrix(A)
		if nx.is_connected(G):
			return nx.diameter(G)
		else:
			raise HyperNetXError(f'Hypergraph is not s-connected. s={s}')

If hypergraph is not s-edge-connected

		Notes
		-----
		Two edges are s-adjacent if they share s nodes.
		Two nodes e_start and e_end are s-walk connected if there is a sequence of
		edges e_start, e_1, e_2, ... e_n-1, e_end such that consecutive edges
		are s-adjacent. If the graph is not connected, an error will be raised.

		"""
		A = self.edge_adjacency_matrix(s=s)
		G = nx.from_scipy_sparse_matrix(A)
		if nx.is_connected(G):
			return nx.diameter(G)
		else:
			raise HyperNetXError(f'Hypergraph is not s-connected. s={s}')

boolean function to be evaluated on each cell of the array
	        
	    Returns
	    -------
	    : Hypergraph

	    Note
	    ----
	    The constructor does not generate empty edges. 
	    All-zero columns in df are removed and the names corresponding to these
	    edges are discarded.    
	    '''
	    import pandas as pd

	    if type(df) != pd.core.frame.DataFrame:
	        raise HyperNetXError('Error: Input object must be a pandas dataframe.') 
	    if transpose:
	        df = df.transpose()   
	    node_names = np.array(df.index)
	    edge_names = np.array(df.columns)
	    df = df.fillna(fillna)
	    if key:
	    	df = df.apply(key)
	    return cls.from_numpy_array(df.values,node_names=node_names,edge_names=edge_names,name=name)

All zero columns in M are removed and the names corresponding to these
		edges are discarded.


		"""  	    
		## Create names for nodes and edges
		## Validate the size of the node and edge arrays

		M = np.array(M)
		if len(M.shape) != (2):
			raise HyperNetXError('Input requires a 2 dimensional numpy array')

		if node_names is not None:
		    nodenames = np.array(node_names)
		    if len(nodenames) != M.shape[0]:
		        raise HyperNetXError('Number of node names does not match number of rows.')
		else:
		    nodenames = np.array([f'v{idx}' for idx in range(M.shape[0])])

		if edge_names is not None:
		    edgenames = np.array(edge_names)
		    if len(edgenames) != M.shape[1]:
		    	raise HyperNetXError('Number of edge_names does not match number of columns.')
		else:
		    edgenames = np.array([f'e{jdx}' for jdx in range(M.shape[1])])

		## apply boolean key if available
		if key:
			M = key(M)

		## Remove empty column indices from M columns and edgenames
		colidx = np.array([jdx for jdx in range(M.shape[1]) if any(M[:,jdx])])

: Hypergraph

		Note
		----
		The constructor does not generate empty edges. 
		All zero columns in M are removed and the names corresponding to these
		edges are discarded.


		"""  	    
		## Create names for nodes and edges
		## Validate the size of the node and edge arrays

		M = np.array(M)
		if len(M.shape) != (2):
			raise HyperNetXError('Input requires a 2 dimensional numpy array')

		if node_names is not None:
		    nodenames = np.array(node_names)
		    if len(nodenames) != M.shape[0]:
		        raise HyperNetXError('Number of node names does not match number of rows.')
		else:
		    nodenames = np.array([f'v{idx}' for idx in range(M.shape[0])])

		if edge_names is not None:
		    edgenames = np.array(edge_names)
		    if len(edgenames) != M.shape[1]:
		    	raise HyperNetXError('Number of edge_names does not match number of columns.')
		else:
		    edgenames = np.array([f'e{jdx}' for jdx in range(M.shape[1])])

		## apply boolean key if available