<｜begin▁of▁sentence｜>### Instruction:
Given a weighted graph with 4 nodes and weighted edges [(1, 2, 0.5), (2, 3, 1.5), (3, 4, 2.5), (4, 1, 1.0)], how can you create a 2D layout using the 'layout_tsne' function from 'graspologic.layouts', taking into account the largest connected component, edge weights, and adjusting for overlap? Please generate the layout with a perplexity of 30, a maximum of 1000 iterations, at most 10,000,000 edges, using 'weight' as the weight attribute, a fixed random seed of 42 for reproducibility, and adjust for overlaps.
### Response:
import networkx as nx
from graspologic.layouts import layout_tsne

# Create a simple weighted graph
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 0.5), (2, 3, 1.5), (3, 4, 2.5), (4, 1, 1.0)])

# Use layout_tsne to create a node2vec embedding, then reduce to 2D with t-SNE
# And get the largest connected component
largest_cc, node_positions = layout_tsne(G, perplexity=30, n_iter=1000, max_edges=10000000, weight_attribute='weight', random_seed=42, adjust_overlaps=True)

# Print results
for pos in node_positions:
    print(f'Node {pos.node_id} at position ({pos.x}, {pos.y}) with size {pos.size}')
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