<｜begin▁of▁sentence｜>### Instruction:
In the context of developing a blockchain-based system for secure transactions, imagine we're visualizing a transaction dependency graph using NetworkX. This graph is acyclic, indicating that each transaction must be confirmed before the next dependent transaction can occur. We've modeled a scenario where we have transactions T0, T1, and T2. 

The transaction confirmations are represented as directed edges: T0 confirms T1, and T1, in turn, confirms T2, each with an associated gas cost of 1 unit. Additionally, there's a direct confirmation from T0 to T2 with a significantly higher gas cost of 42 units. The represented edges are: (T0, T1) with a cost of 1, (T1, T2) with a cost of 1, and (T0, T2) with a cost of 42.

As a blockchain developer trying to optimize for the maximum accumulated gas cost, could you reformulate the task of computing the longest transaction confirmation path based solely on the number of confirmations (edges) and also the longest path considering the cumulative gas costs (edge weights)? This would help us understand the most gas-intensive path in the network which can be crucial for optimizing transaction processing in the blockchain.
### Response:
import networkx as nx

# Create a directed acyclic graph (DAG)
DG = nx.DiGraph()
DG.add_weighted_edges_from([(0, 1, {'cost': 1}), (1, 2, {'cost': 1}), (0, 2, {'cost': 42})])

# Find the longest path length without considering weights
length_no_weights = nx.dag_longest_path_length(DG)

# Find the longest path length considering weights
length_with_weights = nx.dag_longest_path_length(DG, weight='cost')

print('Longest path length without weights:', length_no_weights)
print('Longest path length with weights:', length_with_weights)
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