<｜begin▁of▁sentence｜>### Instruction:
In the meticulous field of radiation therapy, where precision and accuracy are paramount for the effective treatment and well-being of patients, a situation emerges that draws parallels with the tasks carried out by a Radiation Therapist. Imagine you're preparing for a session involving the administration of radiation to combat a malignancy, where the dose intensity and area of impact must be meticulously calibrated to target the tumor cells while sparing the healthy tissue. 

In a similar vein of precision, consider a network scenario where connections mirror the complexity of human anatomy, each assigned a numerical value akin to the dosage intensity. Before beginning an intricate analysis—akin to planning the application of a radiation treatment—you must ensure the integrity of these values.

How, in this scenario, might you confirm that the numerical labels assigned to each link in this network, much like ensuring the proper calibration of a linear accelerator, are indeed real numbers and not symbolic placeholders? This is a crucial step in guaranteeing the subsequent procedures or computations are grounded on a solid foundation of accurate data. Note that the NetworkX API would be the instrument of choice for verifying these numerical weights within the network structure, just as the linear accelerator or IMRT is a tool essential to your profession.
### Response:
import networkx as nx
from graspologic.preconditions import is_real_weighted

# Create a graph
G = nx.Graph()

# Add nodes and weighted edges to the graph
# For simplicity, we'll create a synthetic graph with 6 nodes
G.add_edge(0, 1, weight=1.5)
G.add_edge(0, 2, weight=2.3)
G.add_edge(1, 3, weight=3.1)
G.add_edge(2, 3, weight=0.9)
G.add_edge(3, 4, weight=4.7)
G.add_edge(4, 5, weight=5.6)

# Check if the graph has real weighted edges
weights_are_real = is_real_weighted(G, 'weight')

# Print the result
print('All edges have real weights:', weights_are_real)
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