<｜begin▁of▁sentence｜>### Instruction:
In the process of modeling a bi-dimensional lattice structure within a computational simulation, I've encountered a scenario that requires the adaptation of the lattice parameters before the actual structure is instantiated. The simulation leverages the capabilities of NetworkX, a Python-based library used for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Specifically, the simulation's initialization function, which I refer to as `generate_grid_graph`, is designed to receive two arguments. These arguments represent the dimensions of the lattice - the number of rows and columns. However, there's a prerequisite for the rows parameter: it must undergo a transformation, where its value is squared prior to the construction of the grid graph. The columns parameter remains unaltered.

Thus, I seek an approach, ideally emanating from within the framework afforded by NetworkX, that introduces a methodological decorator. This decorator would elegantly perform the necessary transformation on the rows parameter without directly modifying the main function's signature. Such an adaptation would enable calls like `generate_grid_graph(3, 4)`, implicitly translating to a grid graph of dimensions 9x4.

Could you orchestrate a more concrete example or strategic alteration, maintaining the scientific integrity of my line of inquiry, for the aforementioned algorithmic alteration? This would aid in the seamless progression of my computational research while preserving the semantics of the present infrastructure.
### Response:
from networkx.utils import argmap

# Define a simple transformation function
def square(x):
    return x ** 2

# Apply the transformation to the 'n' parameter using the argmap decorator
@argmap(square, 'n')
def generate_grid_graph(n, m):
    return nx.grid_2d_graph(n, m)

# Now calling generate_grid_graph(3, 4) will actually generate a 9x4 grid graph
grid_graph = generate_grid_graph(3, 4)
nodes_count = len(grid_graph.nodes())
print('Number of nodes in the grid graph:', nodes_count)
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