<｜begin▁of▁sentence｜>### Instruction:
Ladies and gentlemen, gather around as I unveil an exciting simulation of a bustling community network right before your eyes. Picture, if you will, three vibrant communities that we'll represent today in the form of interconnected nodes. The first two communities equal in size, each consisting of 75 individuals, while the third is a thriving metropolis of 300 souls.

Now, in this simulation, our communities come to life as they interact within and among themselves. Imagine the bonds within each community as streets where the likelihood of a connection mirrors that of a neighbourly encounter. For our first community, these encounters happen with a lively 25% chance, creating a tight-knit neighborhood. Our second community flourishes with a substantial 35% chance of interaction, showing the strength of their social fabric. And our third, the largest of all – becomes a network with a robust 40% chance, demonstrating the bustling interactions within.

But that's not all, folks. We understand that communities don't exist in isolation – they interact with each other. And in our demonstration, these interactions are akin to roads between cities – less frequent but equally significant. Though the communities differ in size, they are connected by possibilities – a 5% chance for the first and second, a modest 2% and 7% for interactions between the first and third, and the second and third respectively. 

Let me now introduce the star of our show – the 'stochastic_block_model' function, which, without any smoke and mirrors, will orchestrate these interactions and bring our communities to life in a stunning network display. As the model weaves these probabilities into a tapestry of connections, we will reveal just how many individuals (nodes) and friendships (edges) emerge in this social landscape.

Stay tuned as I compute these marvels and disclose the grand totals of social connections sparkling within our vibrant communities. What you will witness is not just a demonstration but a testament to the power of probabilities in drawing people together, even in a simulated world.
### Response:
import networkx as nx

# Define the sizes of the blocks
sizes = [75, 75, 300]

# Define the probabilities of edges between nodes in the blocks
probs = [[0.25, 0.05, 0.02],
 [0.05, 0.35, 0.07],
 [0.02, 0.07, 0.40]]

# Generate the stochastic block model graph
G = nx.stochastic_block_model(sizes, probs, seed=42)

# Printing information about the graph
print(f'Number of nodes: {G.number_of_nodes()}')
print(f'Number of edges: {G.number_of_edges()}')
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