Class Solution
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g1401_1500.s1489_find_critical_and_pseudo_critical_edges_in_minimum_spanning_tree.Solution
1489 - Find Critical and Pseudo-Critical Edges in Minimum Spanning Tree\.
Hard
Given a weighted undirected connected graph with `n` vertices numbered from `0` to `n - 1`, and an array `edges` where
edges[i] = [ai, bi, weighti] represents a bidirectional and weighted edge between nodes ai and bi. A minimum spanning tree (MST) is a subset of the graph's edges that connects all vertices without cycles and with the minimum possible total edge weight.
Find _all the critical and pseudo-critical edges in the given graph's minimum spanning tree (MST)_. An MST edge whose deletion from the graph would cause the MST weight to increase is called a _critical edge_. On the other hand, a pseudo-critical edge is that which can appear in some MSTs but not all.
Note that you can return the indices of the edges in any order.
**Example 1:**

**Input:** n = 5, edges = \[\[0,1,1],[1,2,1],[2,3,2],[0,3,2],[0,4,3],[3,4,3],[1,4,6]]
**Output:** [[0,1],[2,3,4,5]]
**Explanation:** The figure above describes the graph.
The following figure shows all the possible MSTs:

Notice that the two edges 0 and 1 appear in all MSTs, therefore they are critical edges, so we return them in the first list of the output.
The edges 2, 3, 4, and 5 are only part of some MSTs, therefore they are considered pseudo-critical edges. We add them to the second list of the output.
**Example 2:**

**Input:** n = 4, edges = \[\[0,1,1],[1,2,1],[2,3,1],[0,3,1]]
**Output:** [ [],[0,1,2,3]]
**Explanation:** We can observe that since all 4 edges have equal weight, choosing any 3 edges from the given 4 will yield an MST. Therefore all 4 edges are pseudo-critical.
**Constraints:**
* `2 <= n <= 100`
* `1 <= edges.length <= min(200, n * (n - 1) / 2)`
* `edges[i].length == 3`
* 0 <= ai < bi < n
* 1 <= weighti <= 1000
* All pairs (ai, bi) are **distinct**.-
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Solution
public Solution()
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findCriticalAndPseudoCriticalEdges
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