<jupyter_start><jupyter_text>Clone an undirected graph. Each node in the graph contains a label and a list of its neighbors.OJ's undirected graph serialization:Nodes are labeled uniquely.We use  as a separator for each node, and , as a separator for node label and each neighbor of the node.As an example, consider the serialized graph {0,1,21,22,2}.The graph has a total of three nodes, and therefore contains three parts as separated by .First node is labeled as 0. Connect node 0 to both nodes 1 and 2.Second node is labeled as 1. Connect node 1 to node 2.Third node is labeled as 2. Connect node 2 to node 2 (itself), thus forming a self-cycle.Visually, the graph looks like the following:       1      / \     /   \    0 --- 2         / \         \_/<jupyter_code># Definition for a undirected graph node
# class UndirectedGraphNode:
#     def __init__(self, x):
#         self.label = x
#         self.neighbors = []

class Solution:
    # @param node, a undirected graph node
    # @return a undirected graph node
    def cloneGraph(self, node):
        if not node:return None
        dic = {node:UndirectedGraphNode(node.label)}
        stack = [node]
        while stack:
            new_stack = []
            for parent in stack:
                new_neighbors = []
                for neighbor in parent.neighbors:
                    if not neighbor in dic:
                        dic.update({neighbor:UndirectedGraphNode(neighbor.label)})
                        new_stack.append(neighbor)
                    new_neighbors.append(dic[neighbor])
                dic[parent].neighbors = new_neighbors
            stack = new_stack
        return dic[node]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07239552678045909, 'fraction_numerical': 0.011771630370806356, 'mean_word_length': 2.256704980842912, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '591363', 'license': 'MIT', 'n_tokens_mistral': 492, 'n_tokens_neox': 454, 'n_words': 182, 'path': 'Python/clonegraph.ipynb', 'repo_name': 'AlphaTwist/Code-squad', 'size': '1699'}