This doesn't actually say explicitly how to set the flag. It should probably also give the recipe for fixing these bugs in C code (use `arr->descr->itemsize`). How about:

```
There is a new compile time environment variable ``NPY_RELAXED_STRIDES_CHECKING``. If this variable is set to 1, then numpy will consider more arrays to be C- or F-contiguous -- for example, it becomes possible to have a column vector which is considered both C- and F-contiguous simultaneously. The new definition is more accurate, allows for faster code that makes fewer unnecessary copies, and simplifies numpy's code internally. However, it may also break third-party libraries that make too-strong assumptions about the stride values of C- and F-contiguous arrays. (It is also currently known that this breaks Cython code using memoryviews, which will be fixed in Cython.) THIS WILL BECOME THE DEFAULT IN A FUTURE RELEASE, SO PLEASE TEST YOUR CODE NOW AGAINST NUMPY BUILT WITH::

  NPY_RELAXED_STRIDES_CHECKING=1 python setup.py install

You can check whether NPY_RELAXED_STRIDES_CHECKING is in effect by running::

  np.ones((10, 1), order="C").flags.f_contiguous

This will be ``True`` if relaxed strides checking is enabled, and ``False`` otherwise. The typical problem we've seen so far is C code that works with C-contiguous arrays, and assumes that the itemsize can be accessed by looking at the last element in the ``arr->strides`` array. When relaxed strides are in effect, this is not true (and in fact, it never was true in some corner cases). Instead, use ``arr->descr->itemsize``.

For more information check the "Internal memory layout of an ndarray" section in the documentation.
```