eturns
-------
result : csc_array, csr_array, bsr_array, dia_array or coo_array
    A sparse array/matrix containing the loaded data.

Raises
------
OSError
    If the input file does not exist or cannot be read.

See Also
--------
scipy.sparse.save_npz: Save a sparse array/matrix to a file using ``.npz`` format.
numpy.load: Load several arrays from a ``.npz`` archive.

Examples
--------
Store sparse array/matrix to disk, and load it again:

>>> import numpy as np
>>> import scipy as sp
>>> sparse_array = sp.sparse.csc_array([[0, 0, 3], [4, 0, 0]])
>>> sparse_array
<Compressed Sparse Column sparse array of dtype 'int64'
    with 2 stored elements and shape (2, 3)>
>>> sparse_array.toarray()
array([[0, 0, 3],
       [4, 0, 0]], dtype=int64)

>>> sp.sparse.save_npz('/tmp/sparse_array.npz', sparse_array)
>>> sparse_array = sp.sparse.load_npz('/tmp/sparse_array.npz')

>>> sparse_array
<Compressed Sparse Column sparse array of dtype 'int64'
    with 2 stored elements and shape (2, 3)>
>>> sparse_array.toarray()
array([[0, 0, 3],
       [4, 0, 0]], dtype=int64)

In this example we force the result to be csr_array from csr_matrix
>>> sparse_matrix = sp.sparse.csc_matrix([[0, 0, 3], [4, 0, 0]])
>>> sp.sparse.save_npz('/tmp/sparse_matrix.npz', sparse_matrix)
>>> tmp = sp.sparse.load_npz('/tmp/sparse_matrix.npz')
>>> sparse_array = sp.sparse.csr_array(tmp)
r