<｜begin▁of▁sentence｜>### Instruction:
In the midst of orchestrating the sonic elements for a cutting-edge neuroimaging soundscape, we've stumbled upon a bit of a technical quandary analogous to ensuring the harmony of our audio channels. Picture this: we're fine-tuning the audio mixing board, but before we can transmit our symphony of brain connectivity data through the graspologic pipeline, we have to ascertain that our tracks — or in this context, our 'input_data' — are impeccably aligned in a flawless square formation.

Visualize input_data as a multitrack recording session. Just as each track must be evenly balanced to achieve the perfect mix, so must our matrix exhibit equal dimensions to resonate correctly within the graspologic suite. How might we validate that the array we’ve captured, like a pristine audio sample, forms a matrix with equivalent rows and columns? It's essential to perfection that this check is in place before we proceed with the algorithmic orchestration offered by the graspologic's intricate harmonics.
### Response:
import numpy as np
from graspologic.preconditions import check_argument

# Check if the input data is a square matrix
input_data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
square = (input_data.shape[0] == input_data.shape[1])

# Use the check_argument to ensure this is a square matrix
check_argument(square, 'Input data must be a square matrix.')
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