random number generators perform unnecessary copy

Taking generate as an example:
https://github.com/GalSim-developers/GalSim/blob/ee177aa50424dbbc5cec4a71cadd6297109c2aa1/galsim/random.py#L265-L271

The result of ascontinguousarray can share memory with the input array yet the if array_1d.data != array.data: check can fail resulting in an unnecessary copy.

The issue can be illustrated in the following code:

import numpy as np
array = np.empty((10, 20, 30), dtype=float)
array_1d = np.ascontiguousarray(array.ravel(),dtype=float)

# data is not the same
assert array.data != array_1d.data

# because array_1d is a view of array
assert array.data == array_1d.base.data  # note the use of base here

# arrays share memory
assert np.shares_memory(array, array_1d)

# double check
array_1d[:] = 2
assert np.all(array == 2)

Replacing if array_1d.data != array.data: with if not np.shares_memory(array, array_1d): should fix the issue.

Start in galsim/random.py at generate, especially the linked lines 265-271, and reproduce the NumPy example from the issue to confirm that the flattened array can share memory with the input. Verify that the completed change avoids an unnecessary copy when memory is shared and preserves the expected array contents.