<｜begin▁of▁sentence｜>### Instruction:
As a personal chef, imagine you've been handed a recipe book where each recipe (node) is connected by their flavors or common ingredients (edges), and the intensity of each flavor or ingredient overlap is quantified (weight attribute). Now, you're tasked with ensuring that these intensities (weights) are provided as valid measures that can range from robustly positive to delicately negative, reflecting the complete spectrum of flavor profiles. How would one go about confirming that each of these flavor intensities aligns as a proper real number, thereby affirming that your recipe book operates as a coherent collection of interrelated, real weighted culinary methods? Remember, just like verifying the precise measurements of ingredients in a recipe, use the NetworkX API to inspect the edges of this metaphorical flavor graph.
### Response:
import networkx as nx
from graspologic.preconditions import is_real_weighted

# Create a graph with real weighted edges
G = nx.Graph()
G.add_edge(1, 2, weight=3.5)
G.add_edge(2, 3, weight=4.1)
G.add_edge(3, 4, weight=-2.7)  # Negative weight

# Check if the graph is a real weighted graph
is_real_weighted_graph = is_real_weighted(G, weight_attribute='weight')

print(f'Graph is real weighted: {is_real_weighted_graph}')
<|EOT|>
