def make_array(array_type):
    """
    Return the Structure representation of the given *array_type*
    (an instance of types.ArrayCompatible).

    Note this does not call __array_wrap__ in case a new array structure
    is being created (rather than populated).
    """
    real_array_type = array_type.as_array
    base = cgutils.create_struct_proxy(real_array_type)
    ndim = real_array_type.ndim

    class ArrayStruct(base):

        def _make_refs(self, ref):
            sig = signature(real_array_type, array_type)
            try:
                array_impl = self._context.get_function('__array__', sig)
            except __HOLE__:
                return super(ArrayStruct, self)._make_refs(ref)

            # Return a wrapped structure and its unwrapped reference
            datamodel = self._context.data_model_manager[array_type]
            be_type = self._get_be_type(datamodel)
            if ref is None:
                outer_ref = cgutils.alloca_once(self._builder, be_type, zfill=True)
            else:
                outer_ref = ref
            # NOTE: __array__ is called with a pointer and expects a pointer
            # in return!
            ref = array_impl(self._builder, (outer_ref,))
            return outer_ref, ref

        @property
        def shape(self):
            """
            Override .shape to inform LLVM that its elements are all positive.
            """
            builder = self._builder
            if ndim == 0:
                return base.__getattr__(self, "shape")

            # Unfortunately, we can't use llvm.assume as its presence can
            # seriously pessimize performance,
            # *and* the range metadata currently isn't improving anything here,
            # see https://llvm.org/bugs/show_bug.cgi?id=23848 !
            ptr = self._get_ptr_by_name("shape")
            dims = []
            for i in range(ndim):
                dimptr = cgutils.gep_inbounds(builder, ptr, 0, i)
                load = builder.load(dimptr)
                dims.append(load)
                mark_positive(builder, load)

            return cgutils.pack_array(builder, dims)

    return ArrayStruct
dataset/ETHPy150Open numba/numba/numba/targets/arrayobj.py/make_array