diff --git a/lang/zh/gklearn/ged/env/ged_env.py b/lang/zh/gklearn/ged/env/ged_env.py
new file mode 100644
index 0000000..60b671b
--- /dev/null
+++ b/lang/zh/gklearn/ged/env/ged_env.py
@@ -0,0 +1,733 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jun 17 12:02:36 2020
+
+@author: ljia
+"""
+import numpy as np
+import networkx as nx
+from gklearn.ged.env import Options, OptionsStringMap
+from gklearn.ged.env import GEDData
+
+
+class GEDEnv(object):
+
+	
+	def __init__(self):
+		self.__initialized = False
+		self.__new_graph_ids = []
+		self.__ged_data = GEDData()
+		# Variables needed for approximating ged_instance_.
+		self.__lower_bounds = {}
+		self.__upper_bounds = {}
+		self.__runtimes = {}
+		self.__node_maps = {}
+		self.__original_to_internal_node_ids = []
+		self.__internal_to_original_node_ids = []
+		self.__ged_method = None
+	
+		
+	def set_edit_cost(self, edit_cost, edit_cost_constants=[]):
+		"""
+	/*!
+	 * @brief Sets the edit costs to one of the predefined edit costs.
+	 * @param[in] edit_costs Select one of the predefined edit costs.
+	 * @param[in] edit_cost_constants Constants passed to the constructor of the edit cost class selected by @p edit_costs.
+	 */
+		"""
+		self.__ged_data._set_edit_cost(edit_cost, edit_cost_constants)
+		
+		
+	def add_graph(self, graph_name='', graph_class=''):
+		"""
+	/*!
+	 * @brief Adds a new uninitialized graph to the environment. Call init() after calling this method.
+	 * @param[in] graph_name The name of the added graph. Empty if not specified.
+	 * @param[in] graph_class The class of the added graph. Empty if not specified.
+	 * @return The ID of the newly added graph.
+	 */
+		"""
+		# @todo: graphs are not uninitialized.
+		self.__initialized = False
+		graph_id = self.__ged_data._num_graphs_without_shuffled_copies
+		self.__ged_data._num_graphs_without_shuffled_copies += 1
+		self.__new_graph_ids.append(graph_id)
+		self.__ged_data._graphs.append(nx.Graph())
+		self.__ged_data._graph_names.append(graph_name)
+		self.__ged_data._graph_classes.append(graph_class)
+		self.__original_to_internal_node_ids.append({})
+		self.__internal_to_original_node_ids.append({})
+		self.__ged_data._strings_to_internal_node_ids.append({})
+		self.__ged_data._internal_node_ids_to_strings.append({})
+		return graph_id
+	
+	
+	def clear_graph(self, graph_id):
+		"""
+	/*!
+	 * @brief Clears and de-initializes a graph that has previously been added to the environment. Call init() after calling this method.
+	 * @param[in] graph_id ID of graph that has to be cleared.
+	 */
+		"""
+		if graph_id > self.__ged_data.num_graphs_without_shuffled_copies():
+			raise Exception('The graph', self.get_graph_name(graph_id), 'has not been added to the environment.')
+		self.__ged_data._graphs[graph_id].clear()
+		self.__original_to_internal_node_ids[graph_id].clear()
+		self.__internal_to_original_node_ids[graph_id].clear()
+		self.__ged_data._strings_to_internal_node_ids[graph_id].clear()
+		self.__ged_data._internal_node_ids_to_strings[graph_id].clear()
+		self.__initialized = False
+	
+	
+	def add_node(self, graph_id, node_id, node_label):
+		"""
+	/*!
+	 * @brief Adds a labeled node.
+	 * @param[in] graph_id ID of graph that has been added to the environment.
+	 * @param[in] node_id The user-specific ID of the vertex that has to be added.
+	 * @param[in] node_label The label of the vertex that has to be added. Set to ged::NoLabel() if template parameter @p UserNodeLabel equals ged::NoLabel.
+	 */
+		"""
+		# @todo: check ids.
+		self.__initialized = False
+		internal_node_id = nx.number_of_nodes(self.__ged_data._graphs[graph_id])
+		self.__ged_data._graphs[graph_id].add_node(internal_node_id, label=node_label)
+		self.__original_to_internal_node_ids[graph_id][node_id] = internal_node_id
+		self.__internal_to_original_node_ids[graph_id][internal_node_id] = node_id
+		self.__ged_data._strings_to_internal_node_ids[graph_id][str(node_id)] = internal_node_id
+		self.__ged_data._internal_node_ids_to_strings[graph_id][internal_node_id] = str(node_id)
+		self.__ged_data._node_label_to_id(node_label)
+		label_id = self.__ged_data._node_label_to_id(node_label)
+		# @todo: ged_data_.graphs_[graph_id].set_label
+		
+		
+	def add_edge(self, graph_id, nd_from, nd_to, edge_label, ignore_duplicates=True):
+		"""
+	/*!
+	 * @brief Adds a labeled edge.
+	 * @param[in] graph_id ID of graph that has been added to the environment.
+	 * @param[in] tail The user-specific ID of the tail of the edge that has to be added.
+	 * @param[in] head The user-specific ID of the head of the edge that has to be added.
+	 * @param[in] edge_label The label of the vertex that has to be added. Set to ged::NoLabel() if template parameter @p UserEdgeLabel equals ged::NoLabel.
+	 * @param[in] ignore_duplicates If @p true, duplicate edges are ignores. Otherwise, an exception is thrown if an existing edge is added to the graph.
+	 */
+		"""
+		# @todo: check everything.
+		self.__initialized = False
+		# @todo: check ignore_duplicates.
+		self.__ged_data._graphs[graph_id].add_edge(self.__original_to_internal_node_ids[graph_id][nd_from], self.__original_to_internal_node_ids[graph_id][nd_to], label=edge_label)
+		label_id = self.__ged_data._edge_label_to_id(edge_label)
+		# @todo: ged_data_.graphs_[graph_id].set_label
+		
+	
+	def add_nx_graph(self, g, classe, ignore_duplicates=True) :
+		"""
+			Add a Graph (made by networkx) on the environment. Be careful to respect the same format as GXL graphs for labelling nodes and edges. 
+	
+			:param g: The graph to add (networkx graph)
+			:param ignore_duplicates: If True, duplicate edges are ignored, otherwise it's raise an error if an existing edge is added. True by default
+			:type g: networkx.graph
+			:type ignore_duplicates: bool
+			:return: The ID of the newly added graphe
+			:rtype: size_t
+	
+			.. note:: The NX graph must respect the GXL structure. Please see how a GXL graph is construct.  
+			
+		"""
+		graph_id = self.add_graph(g.name, classe) # check if the graph name already exists.
+		for node in g.nodes: # @todo: if the keys of labels include int and str at the same time.
+			self.add_node(graph_id, node, tuple(sorted(g.nodes[node].items(), key=lambda kv: kv[0])))
+		for edge in g.edges:
+			self.add_edge(graph_id, edge[0], edge[1], tuple(sorted(g.edges[(edge[0], edge[1])].items(), key=lambda kv: kv[0])), ignore_duplicates)
+		return graph_id
+	
+	
+	def load_nx_graph(self, nx_graph, graph_id, graph_name='', graph_class=''):
+		"""
+		Loads NetworkX Graph into the GED environment.
+
+		Parameters
+		----------
+		nx_graph : NetworkX Graph object
+			The graph that should be loaded.
+			
+		graph_id : int or None
+			The ID of a graph contained the environment (overwrite existing graph) or add new graph if `None`.
+																							
+		graph_name : string, optional
+			The name of newly added graph. The default is ''. Has no effect unless `graph_id` equals `None`.
+			
+		graph_class : string, optional
+			The class of newly added graph. The default is ''. Has no effect unless `graph_id` equals `None`.
+
+		Returns
+		-------
+		int
+			The ID of the newly loaded graph.
+		"""
+		if graph_id is None: # @todo: undefined.
+			graph_id = self.add_graph(graph_name, graph_class)
+		else:
+			self.clear_graph(graph_id)
+		for node in nx_graph.nodes:
+			self.add_node(graph_id, node, tuple(sorted(nx_graph.nodes[node].items(), key=lambda kv: kv[0])))
+		for edge in nx_graph.edges:
+			self.add_edge(graph_id, edge[0], edge[1], tuple(sorted(nx_graph.edges[(edge[0], edge[1])].items(), key=lambda kv: kv[0])))
+		return graph_id
+	
+	
+	def init(self, init_type=Options.InitType.EAGER_WITHOUT_SHUFFLED_COPIES, print_to_stdout=False):
+		if isinstance(init_type, str):
+			init_type = OptionsStringMap.InitType[init_type]
+			
+		# Throw an exception if no edit costs have been selected.
+		if self.__ged_data._edit_cost is None:
+			raise Exception('No edit costs have been selected. Call set_edit_cost() before calling init().')
+			
+		# Return if the environment is initialized.
+		if self.__initialized:
+			return
+		
+		# Set initialization type.
+		self.__ged_data._init_type = init_type
+		
+		# @todo: Construct shuffled graph copies if necessary.
+		
+		# Re-initialize adjacency matrices (also previously initialized graphs must be re-initialized because of possible re-allocation).
+		# @todo: setup_adjacency_matrix, don't know if neccessary.
+		self.__ged_data._max_num_nodes = np.max([nx.number_of_nodes(g) for g in self.__ged_data._graphs])
+		self.__ged_data._max_num_edges = np.max([nx.number_of_edges(g) for g in self.__ged_data._graphs])
+			
+		# Initialize cost matrices if necessary.
+		if self.__ged_data._eager_init():
+			pass # @todo: init_cost_matrices_: 1. Update node cost matrix if new node labels have been added to the environment; 2. Update edge cost matrix if new edge labels have been added to the environment.
+			
+		# Mark environment as initialized.
+		self.__initialized = True
+		self.__new_graph_ids.clear()
+		
+		
+	def is_initialized(self):
+		"""
+	/*!
+	 * @brief Check if the environment is initialized.
+	 * @return True if the environment is initialized.
+	 */
+		"""
+		return self.__initialized
+	
+	
+	def get_init_type(self):
+		"""
+	/*!
+	 * @brief Returns the initialization type of the last initialization.
+	 * @return Initialization type.
+	 */
+		"""
+		return self.__ged_data._init_type
+	
+	
+	def set_label_costs(self, node_label_costs=None, edge_label_costs=None):
+		"""Set the costs between labels. 
+		"""
+		if node_label_costs is not None:
+			self.__ged_data._node_label_costs = node_label_costs
+		if edge_label_costs is not None:
+			self.__ged_data._edge_label_costs = edge_label_costs
+		
+		
+	def set_method(self, method, options=''):
+		"""
+	/*!
+	 * @brief Sets the GEDMethod to be used by run_method().
+	 * @param[in] method Select the method that is to be used.
+	 * @param[in] options An options string of the form @"[--@<option@> @<arg@>] [...]@" passed to the selected method.
+	 */
+		"""
+		del self.__ged_method
+		
+		if isinstance(method, str):
+			method = OptionsStringMap.GEDMethod[method]
+
+		if method == Options.GEDMethod.BRANCH:
+			self.__ged_method = Branch(self.__ged_data)
+		elif method == Options.GEDMethod.BRANCH_FAST:
+			self.__ged_method = BranchFast(self.__ged_data)
+		elif method == Options.GEDMethod.BRANCH_FAST:
+			self.__ged_method = BranchFast(self.__ged_data)	
+		elif method == Options.GEDMethod.BRANCH_TIGHT:
+			self.__ged_method = BranchTight(self.__ged_data)	
+		elif method == Options.GEDMethod.BRANCH_UNIFORM:
+			self.__ged_method = BranchUniform(self.__ged_data)	
+		elif method == Options.GEDMethod.BRANCH_COMPACT:
+			self.__ged_method = BranchCompact(self.__ged_data)	
+		elif method == Options.GEDMethod.PARTITION:
+			self.__ged_method = Partition(self.__ged_data)	
+		elif method == Options.GEDMethod.HYBRID:
+			self.__ged_method = Hybrid(self.__ged_data)	
+		elif method == Options.GEDMethod.RING:
+			self.__ged_method = Ring(self.__ged_data)	
+		elif method == Options.GEDMethod.ANCHOR_AWARE_GED:
+			self.__ged_method = AnchorAwareGED(self.__ged_data)	
+		elif method == Options.GEDMethod.WALKS:
+			self.__ged_method = Walks(self.__ged_data)	
+		elif method == Options.GEDMethod.IPFP:
+			self.__ged_method = IPFP(self.__ged_data)	
+		elif method == Options.GEDMethod.BIPARTITE:
+			from gklearn.ged.methods import Bipartite
+			self.__ged_method = Bipartite(self.__ged_data)	
+		elif method == Options.GEDMethod.SUBGRAPH:
+			self.__ged_method = Subgraph(self.__ged_data)	
+		elif method == Options.GEDMethod.NODE:
+			self.__ged_method = Node(self.__ged_data)	
+		elif method == Options.GEDMethod.RING_ML:
+			self.__ged_method = RingML(self.__ged_data)	
+		elif method == Options.GEDMethod.BIPARTITE_ML:
+			self.__ged_method = BipartiteML(self.__ged_data)	
+		elif method == Options.GEDMethod.REFINE:
+			self.__ged_method = Refine(self.__ged_data)	
+		elif method == Options.GEDMethod.BP_BEAM:
+			self.__ged_method = BPBeam(self.__ged_data)	
+		elif method == Options.GEDMethod.SIMULATED_ANNEALING:
+			self.__ged_method = SimulatedAnnealing(self.__ged_data)	
+		elif method == Options.GEDMethod.HED:
+			self.__ged_method = HED(self.__ged_data)	
+		elif method == Options.GEDMethod.STAR:
+			self.__ged_method = STAR(self.__ged_data)	
+		# #ifdef GUROBI
+		elif method == Options.GEDMethod.F1:
+			self.__ged_method = F1(self.__ged_data)	
+		elif method == Options.GEDMethod.F2:
+			self.__ged_method = F2(self.__ged_data)	
+		elif method == Options.GEDMethod.COMPACT_MIP:
+			self.__ged_method = CompactMIP(self.__ged_data)	
+		elif method == Options.GEDMethod.BLP_NO_EDGE_LABELS:
+			self.__ged_method = BLPNoEdgeLabels(self.__ged_data)	
+
+		self.__ged_method.set_options(options)
+		
+		
+	def run_method(self, g_id, h_id):
+		"""
+	/*!
+	 * @brief Runs the GED method specified by call to set_method() between the graphs with IDs @p g_id and @p h_id.
+	 * @param[in] g_id ID of an input graph that has been added to the environment.
+	 * @param[in] h_id ID of an input graph that has been added to the environment.
+	 */
+		"""
+		if g_id >= self.__ged_data.num_graphs():
+			raise Exception('The graph with ID', str(g_id), 'has not been added to the environment.')
+		if h_id >= self.__ged_data.num_graphs():
+			raise Exception('The graph with ID', str(h_id), 'has not been added to the environment.')
+		if not self.__initialized:
+			raise Exception('The environment is uninitialized. Call init() after adding all graphs to the environment.')
+		if self.__ged_method is None:
+			raise Exception('No method has been set. Call set_method() before calling run().')
+		
+		# Call selected GEDMethod and store results.
+		if self.__ged_data.shuffled_graph_copies_available() and (g_id == h_id):
+			self.__ged_method.run(g_id, self.__ged_data.id_shuffled_graph_copy(h_id)) # @todo: why shuffle?
+		else:
+			self.__ged_method.run(g_id, h_id)
+		self.__lower_bounds[(g_id, h_id)] = self.__ged_method.get_lower_bound()
+		self.__upper_bounds[(g_id, h_id)] = self.__ged_method.get_upper_bound()
+		self.__runtimes[(g_id, h_id)] = self.__ged_method.get_runtime()
+		self.__node_maps[(g_id, h_id)] = self.__ged_method.get_node_map()
+		
+		
+	def init_method(self):
+		"""Initializes the method specified by call to set_method().
+		"""
+		if not self.__initialized:
+			raise Exception('The environment is uninitialized. Call init() before calling init_method().')
+		if self.__ged_method is None:
+			raise Exception('No method has been set. Call set_method() before calling init_method().')
+		self.__ged_method.init()
+		
+		
+	def get_num_node_labels(self):
+		"""
+	/*!
+	 * @brief Returns the number of node labels.
+	 * @return Number of pairwise different node labels contained in the environment.
+	 * @note If @p 1 is returned, the nodes are unlabeled.
+	 */
+		"""
+		return len(self.__ged_data._node_labels)
+	
+	
+	def get_all_node_labels(self):
+		"""
+	/*!
+	 * @brief Returns the list of all node labels.
+	 * @return List of pairwise different node labels contained in the environment.
+	 * @note If @p 1 is returned, the nodes are unlabeled.
+	 */
+		"""
+		return self.__ged_data._node_labels
+	
+	
+	def get_node_label(self, label_id, to_dict=True):
+		"""
+	/*!
+	 * @brief Returns node label.
+	 * @param[in] label_id ID of node label that should be returned. Must be between 1 and num_node_labels().
+	 * @return Node label for selected label ID.
+	 */
+		"""
+		if label_id < 1 or label_id > self.get_num_node_labels():
+			raise Exception('The environment does not contain a node label with ID', str(label_id), '.')
+		if to_dict:
+			return dict(self.__ged_data._node_labels[label_id - 1])
+		return self.__ged_data._node_labels[label_id - 1]
+		
+		
+	def get_num_edge_labels(self):
+		"""
+	/*!
+	 * @brief Returns the number of edge labels.
+	 * @return Number of pairwise different edge labels contained in the environment.
+	 * @note If @p 1 is returned, the edges are unlabeled.
+	 */
+		"""
+		return len(self.__ged_data._edge_labels)
+	
+	
+	def get_all_edge_labels(self):
+		"""
+	/*!
+	 * @brief Returns the list of all edge labels.
+	 * @return List of pairwise different edge labels contained in the environment.
+	 * @note If @p 1 is returned, the edges are unlabeled.
+	 */
+		"""
+		return self.__ged_data._edge_labels
+	
+		
+	def get_edge_label(self, label_id, to_dict=True):
+		"""
+	/*!
+	 * @brief Returns edge label.
+	 * @param[in] label_id ID of edge label that should be returned. Must be between 1 and num_node_labels().
+	 * @return Edge label for selected label ID.
+	 */
+		"""
+		if label_id < 1 or label_id > self.get_num_edge_labels():
+			raise Exception('The environment does not contain an edge label with ID', str(label_id), '.')
+		if to_dict:
+			return dict(self.__ged_data._edge_labels[label_id - 1])
+		return self.__ged_data._edge_labels[label_id - 1]
+	
+		
+	def get_upper_bound(self, g_id, h_id):
+		"""
+	/*!
+	 * @brief Returns upper bound for edit distance between the input graphs.
+	 * @param[in] g_id ID of an input graph that has been added to the environment.
+	 * @param[in] h_id ID of an input graph that has been added to the environment.
+	 * @return Upper bound computed by the last call to run_method() with arguments @p g_id and @p h_id.
+	 */
+		"""
+		if (g_id, h_id) not in self.__upper_bounds:
+			raise Exception('Call run(' + str(g_id) + ',' + str(h_id) + ') before calling get_upper_bound(' + str(g_id) + ',' + str(h_id) + ').')
+		return self.__upper_bounds[(g_id, h_id)]
+		
+		
+	def get_lower_bound(self, g_id, h_id):
+		"""
+	/*!
+	 * @brief Returns lower bound for edit distance between the input graphs.
+	 * @param[in] g_id ID of an input graph that has been added to the environment.
+	 * @param[in] h_id ID of an input graph that has been added to the environment.
+	 * @return Lower bound computed by the last call to run_method() with arguments @p g_id and @p h_id.
+	 */
+		"""
+		if (g_id, h_id) not in self.__lower_bounds:
+			raise Exception('Call run(' + str(g_id) + ',' + str(h_id) + ') before calling get_lower_bound(' + str(g_id) + ',' + str(h_id) + ').')
+		return self.__lower_bounds[(g_id, h_id)]
+		
+		
+	def get_runtime(self, g_id, h_id):
+		"""
+	/*!
+	 * @brief Returns runtime.
+	 * @param[in] g_id ID of an input graph that has been added to the environment.
+	 * @param[in] h_id ID of an input graph that has been added to the environment.
+	 * @return Runtime of last call to run_method() with arguments @p g_id and @p h_id.
+	 */
+		"""
+		if (g_id, h_id) not in self.__runtimes:
+			raise Exception('Call run(' + str(g_id) + ',' + str(h_id) + ') before calling get_runtime(' + str(g_id) + ',' + str(h_id) + ').')
+		return self.__runtimes[(g_id, h_id)]
+	
+
+	def get_init_time(self):
+		"""
+	/*!
+	 * @brief Returns initialization time.
+	 * @return Runtime of the last call to init_method().
+	 */
+		"""		
+		return self.__ged_method.get_init_time()
+
+
+	def get_node_map(self, g_id, h_id):
+		"""
+	/*!
+	 * @brief Returns node map between the input graphs.
+	 * @param[in] g_id ID of an input graph that has been added to the environment.
+	 * @param[in] h_id ID of an input graph that has been added to the environment.
+	 * @return Node map computed by the last call to run_method() with arguments @p g_id and @p h_id.
+	 */
+		"""
+		if (g_id, h_id) not in self.__node_maps:
+			raise Exception('Call run(' + str(g_id) + ',' + str(h_id) + ') before calling get_node_map(' + str(g_id) + ',' + str(h_id) + ').')
+		return self.__node_maps[(g_id, h_id)]
+	
+
+	def get_forward_map(self, g_id, h_id) :
+		"""
+			Returns the forward map (or the half of the adjacence matrix) between nodes of the two indicated graphs. 
+	
+			:param g: The Id of the first compared graph 
+			:param h: The Id of the second compared graph
+			:type g: size_t
+			:type h: size_t
+			:return: The forward map to the adjacence matrix between nodes of the two graphs
+			:rtype: list[npy_uint32]
+			
+			.. seealso:: run_method(), get_upper_bound(), get_lower_bound(), get_backward_map(), get_runtime(), quasimetric_cost(), get_node_map(), get_assignment_matrix()
+			.. warning:: run_method() between the same two graph must be called before this function. 
+			.. note:: I don't know how to connect the two map to reconstruct the adjacence matrix. Please come back when I know how it's work ! 
+		"""
+		return self.get_node_map(g_id, h_id).forward_map
+	
+	
+	def get_backward_map(self, g_id, h_id) :
+		"""
+			Returns the backward map (or the half of the adjacence matrix) between nodes of the two indicated graphs. 
+	
+			:param g: The Id of the first compared graph 
+			:param h: The Id of the second compared graph
+			:type g: size_t
+			:type h: size_t
+			:return: The backward map to the adjacence matrix between nodes of the two graphs
+			:rtype: list[npy_uint32]
+			
+			.. seealso:: run_method(), get_upper_bound(), get_lower_bound(),  get_forward_map(), get_runtime(), quasimetric_cost(), get_node_map(), get_assignment_matrix()
+			.. warning:: run_method() between the same two graph must be called before this function. 
+			.. note:: I don't know how to connect the two map to reconstruct the adjacence matrix. Please come back when I know how it's work ! 
+		"""
+		return self.get_node_map(g_id, h_id).backward_map
+	
+	
+	def compute_induced_cost(self, g_id, h_id, node_map):
+		"""
+	/*!
+	 * @brief Computes the edit cost between two graphs induced by a node map.
+	 * @param[in] g_id ID of input graph.
+	 * @param[in] h_id ID of input graph.
+	 * @param[in,out] node_map Node map whose induced edit cost is to be computed.
+	 */
+		"""
+		self.__ged_data.compute_induced_cost(self.__ged_data._graphs[g_id], self.__ged_data._graphs[h_id], node_map)
+	
+	
+	def get_nx_graph(self, graph_id):
+		"""
+	 * @brief Returns NetworkX.Graph() representation.
+	 * @param[in] graph_id ID of the selected graph.
+		"""
+		graph = nx.Graph() # @todo: add graph attributes.
+		graph.graph['id'] = graph_id
+		
+		nb_nodes = self.get_graph_num_nodes(graph_id)
+		original_node_ids = self.get_original_node_ids(graph_id)
+		node_labels = self.get_graph_node_labels(graph_id, to_dict=True)
+		graph.graph['original_node_ids'] = original_node_ids		
+		
+		for node_id in range(0, nb_nodes):
+			graph.add_node(node_id, **node_labels[node_id])
+			
+		edges = self.get_graph_edges(graph_id, to_dict=True)
+		for (head, tail), labels in edges.items():
+			graph.add_edge(head, tail, **labels)
+
+		return graph
+	
+	
+	def get_graph_node_labels(self, graph_id, to_dict=True):
+		"""
+			Searchs and returns all the labels of nodes on a graph, selected by its ID. 
+	
+			:param graph_id: The ID of the wanted graph
+			:type graph_id: size_t
+			:return: The list of nodes' labels on the selected graph
+			:rtype: list[dict{string : string}]
+			
+			.. seealso:: get_graph_internal_id(), get_graph_num_nodes(), get_graph_num_edges(), get_original_node_ids(), get_graph_edges(), get_graph_adjacence_matrix()
+			.. note:: These functions allow to collect all the graph's informations.
+		"""
+		graph = self.__ged_data.graph(graph_id)
+		node_labels = []
+		for n in graph.nodes():
+			node_labels.append(graph.nodes[n]['label'])
+		if to_dict:
+			return [dict(i) for i in node_labels]
+		return node_labels
+	
+	
+	def get_graph_edges(self, graph_id, to_dict=True):
+		"""
+			Searchs and returns all the edges on a graph, selected by its ID. 
+	
+			:param graph_id: The ID of the wanted graph
+			:type graph_id: size_t
+			:return: The list of edges on the selected graph
+			:rtype: dict{tuple(size_t, size_t) : dict{string : string}}
+			
+			.. seealso::get_graph_internal_id(), get_graph_num_nodes(), get_graph_num_edges(), get_original_node_ids(), get_graph_node_labels(), get_graph_adjacence_matrix()
+			.. note:: These functions allow to collect all the graph's informations.
+		"""
+		graph = self.__ged_data.graph(graph_id)		
+		if to_dict:
+			edges = {}		
+			for n1, n2, attr in graph.edges(data=True):
+				edges[(n1, n2)] = dict(attr['label'])
+			return edges
+		return {(n1, n2): attr['label'] for n1, n2, attr in graph.edges(data=True)}
+
+	
+	
+	def get_graph_name(self, graph_id):
+		"""
+	/*!
+	 * @brief Returns the graph name.
+	 * @param[in] graph_id ID of an input graph that has been added to the environment.
+	 * @return Name of the input graph.
+	 */
+		"""
+		return self.__ged_data._graph_names[graph_id]
+	
+	
+	def get_graph_num_nodes(self, graph_id):
+		"""
+	/*!
+	 * @brief Returns the number of nodes.
+	 * @param[in] graph_id ID of an input graph that has been added to the environment.
+	 * @return Number of nodes in the graph.
+	 */
+		"""
+		return nx.number_of_nodes(self.__ged_data.graph(graph_id))
+	
+	
+	def get_original_node_ids(self, graph_id):
+		"""
+			Searchs and returns all th Ids of nodes on a graph, selected by its ID. 
+	
+			:param graph_id: The ID of the wanted graph
+			:type graph_id: size_t
+			:return: The list of IDs's nodes on the selected graph
+			:rtype: list[string]
+			
+			.. seealso::get_graph_internal_id(), get_graph_num_nodes(), get_graph_num_edges(), get_graph_node_labels(), get_graph_edges(), get_graph_adjacence_matrix()
+			.. note:: These functions allow to collect all the graph's informations.
+		"""
+		return [i for i in self.__internal_to_original_node_ids[graph_id].values()]		
+	
+	
+	def get_node_cost(self, node_label_1, node_label_2):
+		return self.__ged_data.node_cost(node_label_1, node_label_2)
+	
+	
+	def get_node_rel_cost(self, node_label_1, node_label_2):
+		"""
+	/*!
+	 * @brief Returns node relabeling cost.
+	 * @param[in] node_label_1 First node label.
+	 * @param[in] node_label_2 Second node label.
+	 * @return Node relabeling cost for the given node labels.
+	 */
+		"""
+		if isinstance(node_label_1, dict):
+			node_label_1 = tuple(sorted(node_label_1.items(), key=lambda kv: kv[0]))
+		if isinstance(node_label_2, dict):
+			node_label_2 = tuple(sorted(node_label_2.items(), key=lambda kv: kv[0]))
+		return self.__ged_data._edit_cost.node_rel_cost_fun(node_label_1, node_label_2) # @todo: may need to use node_cost() instead (or change node_cost() and modify ged_method for pre-defined cost matrices.)
+		
+		
+	def get_node_del_cost(self, node_label):
+		"""
+	/*!
+	 * @brief Returns node deletion cost.
+	 * @param[in] node_label Node label.
+	 * @return Cost of deleting node with given label.
+	 */
+		"""
+		if isinstance(node_label, dict):
+			node_label = tuple(sorted(node_label.items(), key=lambda kv: kv[0]))
+		return self.__ged_data._edit_cost.node_del_cost_fun(node_label)
+		
+		
+	def get_node_ins_cost(self, node_label):
+		"""
+	/*!
+	 * @brief Returns node insertion cost.
+	 * @param[in] node_label Node label.
+	 * @return Cost of inserting node with given label.
+	 */
+		"""
+		if isinstance(node_label, dict):
+			node_label = tuple(sorted(node_label.items(), key=lambda kv: kv[0]))
+		return self.__ged_data._edit_cost.node_ins_cost_fun(node_label)
+	
+	
+	def get_edge_cost(self, edge_label_1, edge_label_2):
+		return self.__ged_data.edge_cost(edge_label_1, edge_label_2)
+		
+		
+	def get_edge_rel_cost(self, edge_label_1, edge_label_2):
+		"""
+	/*!
+	 * @brief Returns edge relabeling cost.
+	 * @param[in] edge_label_1 First edge label.
+	 * @param[in] edge_label_2 Second edge label.
+	 * @return Edge relabeling cost for the given edge labels.
+	 */
+		"""
+		if isinstance(edge_label_1, dict):
+			edge_label_1 = tuple(sorted(edge_label_1.items(), key=lambda kv: kv[0]))
+		if isinstance(edge_label_2, dict):
+			edge_label_2 = tuple(sorted(edge_label_2.items(), key=lambda kv: kv[0]))
+		return self.__ged_data._edit_cost.edge_rel_cost_fun(edge_label_1, edge_label_2)
+		
+		
+	def get_edge_del_cost(self, edge_label):
+		"""
+	/*!
+	 * @brief Returns edge deletion cost.
+	 * @param[in] edge_label Edge label.
+	 * @return Cost of deleting edge with given label.
+	 */
+		"""
+		if isinstance(edge_label, dict):
+			edge_label = tuple(sorted(edge_label.items(), key=lambda kv: kv[0]))
+		return self.__ged_data._edit_cost.edge_del_cost_fun(edge_label)
+		
+		
+	def get_edge_ins_cost(self, edge_label):
+		"""
+	/*!
+	 * @brief Returns edge insertion cost.
+	 * @param[in] edge_label Edge label.
+	 * @return Cost of inserting edge with given label.
+	 */
+		"""
+		if isinstance(edge_label, dict):
+			edge_label = tuple(sorted(edge_label.items(), key=lambda kv: kv[0]))
+		return self.__ged_data._edit_cost.edge_ins_cost_fun(edge_label)
+		
+		
+	def get_all_graph_ids(self):
+		return [i for i in range(0, self.__ged_data._num_graphs_without_shuffled_copies)]
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