Update docstring to be in the method docstring and not just in the doc.

doc/library/tensor/basic.txt

@@ -754,40 +754,7 @@ Creating Tensor
     >>> f(x, x, x, x).shape
     (2, 2, 4, 4)
 
-.. function:: choose(a, choices, out=None, mode='raise')
-
-    Construct an array from an index array and a set of arrays to choose from.
-
-    First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description (below ndi = numpy.lib.index_tricks):
-
-    np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)]).
-
-    But this omits some subtleties. Here is a fully general summary:
-
-    Given an “index” array (a) of integers and a sequence of n arrays (choices), a and each choice array are first broadcast, as necessary, to arrays of a common shape; calling these Ba and Bchoices[i], i = 0,...,n-1 we have that, necessarily, Ba.shape == Bchoices[i].shape for each i. Then, a new array with shape Ba.shape is created as follows:
-
-    if mode=raise (the default), then, first of all, each element of a (and thus Ba) must be in the range [0, n-1]; now, suppose that i (in that range) is the value at the (j0, j1, ..., jm) position in Ba - then the value at the same position in the new array is the value in Bchoices[i] at that same position;
-    if mode=wrap, values in a (and thus Ba) may be any (signed) integer; modular arithmetic is used to map integers outside the range [0, n-1] back into that range; and then the new array is constructed as above;
-    if mode=clip, values in a (and thus Ba) may be any (signed) integer; negative integers are mapped to 0; values greater than n-1 are mapped to n-1; and then the new array is constructed as above.
-
-    Parameters : 
-    a : int array
-        This array must contain integers in [0, n-1], where n is the number of choices, unless mode=wrap or mode=clip, in which cases any integers are permissible. 
-    choices : sequence of arrays
-        Choice arrays. a and all of the choices must be broadcastable to the same shape. If choices is itself an array (not recommended), then its outermost dimension (i.e., the one corresponding to choices.shape[0]) is taken as defining the “sequence”.
-    out : array, optional
-        If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.
-    mode : {‘raise’ (default), ‘wrap’, ‘clip’}, optional
-        Specifies how indices outside [0, n-1] will be treated:
-        ‘raise’ : an exception is raised
-        ‘wrap’ : value becomes value mod n
-        ‘clip’ : values < 0 are mapped to 0, values > n-1 are mapped to n-1
-    Returns :
-    merged_array : array
-        The merged result.
-    Raises :
-        ValueError: shape mismatch
-        If a and each choice array are not all broadcastable to the same shape.
+.. autofunction:: theano.tensor.basic.choose
 
 Reductions
 ==========

theano/tensor/basic.py

@@ -5095,6 +5095,38 @@ def swapaxes(y, axis1, axis2):
 
 
 def choose(a, choices, out=None, mode='raise'):
+    """
+    Construct an array from an index array and a set of arrays to choose from.
+
+    First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description (below ndi = numpy.lib.index_tricks):
+
+    np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)]).
+
+    But this omits some subtleties. Here is a fully general summary:
+
+    Given an ``index`` array (a) of integers and a sequence of n arrays (choices), a and each choice array are first broadcast, as necessary, to arrays of a common shape; calling these Ba and Bchoices[i], i = 0,...,n-1 we have that, necessarily, Ba.shape == Bchoices[i].shape for each i. Then, a new array with shape Ba.shape is created as follows:
+
+    if mode=raise (the default), then, first of all, each element of a (and thus Ba) must be in the range [0, n-1]; now, suppose that i (in that range) is the value at the (j0, j1, ..., jm) position in Ba - then the value at the same position in the new array is the value in Bchoices[i] at that same position;
+    if mode=wrap, values in a (and thus Ba) may be any (signed) integer; modular arithmetic is used to map integers outside the range [0, n-1] back into that range; and then the new array is constructed as above;
+    if mode=clip, values in a (and thus Ba) may be any (signed) integer; negative integers are mapped to 0; values greater than n-1 are mapped to n-1; and then the new array is constructed as above.
+
+    :Parameters: *a* - int array
+        This array must contain integers in [0, n-1], where n is the number of choices, unless mode=wrap or mode=clip, in which cases any integers are permissible.
+    :Parameters: *choices* - sequence of arrays
+        Choice arrays. a and all of the choices must be broadcastable to the same shape. If choices is itself an array (not recommended), then its outermost dimension (i.e., the one corresponding to choices.shape[0]) is taken as defining the ``sequence``.
+    :Parameters: *out* - array, optional
+        If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.
+    :Parameters: *mode* - {``raise`` (default), ``wrap``, ``clip``}, optional
+        Specifies how indices outside [0, n-1] will be treated:
+        ``raise`` : an exception is raised
+        ``wrap`` : value becomes value mod n
+        ``clip`` : values < 0 are mapped to 0, values > n-1 are mapped to n-1
+    :Returns: merged_array - array
+        The merged result.
+    :Raises:
+        ValueError - shape mismatch
+        If a and each choice array are not all broadcastable to the same shape.
+    """
     # This is done to keep the same function signature then NumPy.
     assert out is None
     return Choose(mode)(a, choices)