Trac #21156: AbstractLinearCode.dimension() is wrong if _dimension is not set

The current implementation of `AbstractLinearCode.dimension()` simply
accesses `self._dimension`. But there is no (longer a) requirement that
this be set at construction time. The method should instead check if the
special variable is set, and if it is not, compute the dimension as the
number of rows in a generator matrix.

URL: https://trac.sagemath.org/21156
Reported by: jsrn
Ticket author(s): Bruno Grenet
Reviewer(s): Johan Rosenkilde

Author: Release Manager

src/sage/coding/linear_code.py

@@ -326,6 +326,12 @@ class AbstractLinearCode(Module):
       You need of course to complete the constructor by adding any additional parameter
       needed to describe properly the code defined in the subclass.
 
+    - Add the following two lines on the class level::
+
+          _registered_encoders = {}
+          _registered_decoders = {}
+
+
     - fill the dictionary of its encoders in ``sage.coding.__init__.py`` file. Example:
       I want to link the encoder ``MyEncoderClass`` to ``MyNewCodeClass``
       under the name ``MyEncoderName``.
@@ -1554,8 +1560,38 @@ def dimension(self):
             sage: C = LinearCode(G)
             sage: C.dimension()
             2
+
+        TESTS:
+
+        Check that :trac:`21156` is fixed::
+
+            sage: from sage.coding.linear_code import AbstractLinearCode
+            sage: from sage.coding.encoder import Encoder
+            sage: class MonkeyCode(AbstractLinearCode):
+            ....:     _registered_encoders = {}
+            ....:     _registered_decoders = {}
+            ....:     def __init__(self):
+            ....:         super(MonkeyCode, self).__init__(GF(5), 10, "Monkey", "Syndrome")
+            ....:
+            sage: class MonkeyEncoder(Encoder):
+            ....:     def __init__(self, code):
+            ....:         super(MonkeyEncoder, self).__init__(C)
+            ....:     @cached_method
+            ....:     def generator_matrix(self):
+            ....:         G = identity_matrix(GF(5), 5).augment(matrix(GF(5), 5, 7))
+            ....:         return G
+            ....:
+            sage: MonkeyCode._registered_encoders["Monkey"] = MonkeyEncoder
+            sage: C = MonkeyCode()
+            sage: C.dimension()
+            5
         """
-        return self._dimension
+        try:
+            return self._dimension
+        except AttributeError:
+            dimension = self.generator_matrix().nrows()
+            self._dimension = dimension
+            return self._dimension
 
     def direct_sum(self, other):
         """