[Feature Request] Add missing functions in numpy backend.

🚀 Feature

There are some missing or incompatible functions compared with the interface in numpy backend. The list is following.

Missing

• device_type
• mean
• unsorted_1d_segment_sum
• unsorted_1d_segment_mean
• zerocopy_to_dgl_ndarray
• logical_not
• zerocopy_to_dlpack
• equal
• ndim
• sync
• zerocopy_from_dlpack
• copy_reduce
• zerocopy_from_dgl_ndarray
• boolean_mask
• narrow_row
• stack
• binary_reduce
• device_id
• zeros_like

Not compatible

• zeros (shape, dtype) v.s. (shape, dtype, ctx)
• ones (shape, dtype)v.s. (shape, dtype, ctx)
• full_1d (length, fill_value) v.s. (length, fill_value, dtype, ctx)

Motivation

In some scenarios, we just want to use message propogation in dgl (e.g. LPA, PageRank) and thus choose numpy as backend. However due to the missing or incompatible functions in numpy backend, even the simplest example send(message_func=dgl.function.copy_src("f", "m")) will raise an exception.

Do you think it's necessary to completing the missing functions? And if so I can help to add some within my ability.