Lambdify misinterprets some matrix expressions

Using lambdify on an expression containing an identity matrix gives us an unexpected result:

>>> import numpy as np
>>> n = symbols('n', integer=True)
>>> A = MatrixSymbol("A", n, n)
>>> a = np.array([[1, 2], [3, 4]])
>>> f = lambdify(A, A + Identity(n))
>>> f(a)
array([[1.+1.j, 2.+1.j],
       [3.+1.j, 4.+1.j]])

Instead, the output should be array([[2, 2], [3, 5]]), since we're adding an identity matrix to the array. Inspecting the globals and source code of f shows us why we get the result:

>>> import inspect
>>> print(inspect.getsource(f))
def _lambdifygenerated(A):
    return (I + A)
>>> f.__globals__['I']
1j

The code printer prints I, which is currently being interpreted as a Python built-in complex number. The printer should support printing identity matrices, and signal an error for unsupported expressions that might be misinterpreted.