def _argminmax(fn, a, axis=None):
a = np_array_ops.array(a)
if axis is None:
# When axis is None numpy flattens the array.
a_t = array_ops.reshape(a.data, [-1])
else:
a_t = np_array_ops.atleast_1d(a).data
return np_utils.tensor_to_ndarray(fn(input=a_t, axis=axis))
def _argminmax(fn, a, axis=None):
a = np_array_ops.array(a)
if axis is None:
# When axis is None numpy flattens the array.
a_t = array_ops.reshape(a, [-1])
else:
a_t = np_array_ops.atleast_1d(a)
return fn(input=a_t, axis=axis)
