Measure FLOPs from Numpy operations

Question

Is there a general way of measuring how many floating-point operations a sequence of Numpy commands involves?—commands such as np.random.randint, np.sum, np.argmin, np.matmul or @, etc.

Or is the only way to do it manually, thinking from a pure mathematical standpoint and/or looking at how Numpy implements the functions, as follows:

• matrix multiplication involves (2p - 1)mn FLOPs, if we multiply a m × p matrix by a p × n matrix
• argmin involves O(n) ≈ cn comparisons for an array of length n, but what should c be? I tried looking at the Numpy source code but I'm fairly confused about how _wrapfunc is supposed to work or what C code is relevant here.
• etc.