This patch adds an extra base handle parameter to most ``py::array`` and
``py::array_t<>`` constructors. If specified along with a pointer to
data, the base object will be registered within NumPy, which increases
the base's reference count. This feature is useful to create shallow
copies of C++ or Python arrays while ensuring that the owners of the
underlying can't be garbage collected while referenced by NumPy.

The commit also adds a simple test function involving a ``wrap()``
function that creates shallow copies of various N-D arrays.

- This actually works with no changes, I just wasn't 100% convinced and
  decided to write a test to see if it's true.

extra python version sanity check at import time

Python 3.5 can often import pybind11 modules compiled compiled for
Python 3.4 (i.e. all symbols can be resolved), but this leads to crashes
later on due to changes in various Python-internal data structures. This
commit adds an extra sanity check to prevent a successful import when
the Python versions don't match.

This fixes an issue that can arise when forwarding (*args, **kwargs)
captured from a pybind11-bound function call to another Python function.
When the initial function call includes no keyword arguments, the
py::kwargs field is set to nullptr and causes a crash later on.

Re-add accessor bool operator

PR #425 removed the bool operator from attribute accessors.  This is
likely in use by existing code as it was the only way before #425 added
the `hasattr` function to check for the existence of an attribute, via:

    if (obj.attr("foo")) { ... }

This commit adds it back in for attr and item accessors, but with a
deprecation warning to use `hasattr(obj, ...)` or `obj.contains(...)`
instead.

Simplify base class detection for Eigen types

Add in casts for c++11s chrono classes to pythons datetime

Make the accessor interface more complete

`auto var = l[0]` has a strange quirk: `var` is actually an accessor and
not an object, so any later assignment of `var = ...` would modify l[0]
instead of `var`. This is surprising compared to the non-auto assignment
`py::object var = l[0]; var = ...`.

By overloading `operator=` on lvalue/rvalue, the expected behavior is
restored even for `auto` variables.

This also adds the `hasattr` and `getattr` functions which are needed
with the new attribute behavior. The new functions behave exactly like
their Python counterparts.

Similarly `object` gets a `contains` method which calls `__contains__`,
i.e. it's the same as the `in` keyword in Python.

Use consistent indentation and typenames in numpy vectorize. 

Fix minor documentation spelling mistakes

Fix missing smart_ptr test

WIP: Multiple inheritance support