implementing the suggested changes

Author: aman-coder03

pymc/distributions/distribution.py

@@ -521,7 +521,7 @@ def __new__(
         # finally, observed, to determine the shape of the variable.
         if kwargs.get("size") is None and kwargs.get("shape") is None:
             if dims is not None:
-                kwargs["shape"] = model.shape_from_dims(dims)
+                kwargs["shape"] = model.symbolic_shape_from_dims(dims)
             elif observed is not None:
                 kwargs["shape"] = tuple(observed.shape)
 

pymc/model/core.py

@@ -922,7 +922,7 @@ def dim_lengths(self) -> dict[str, TensorVariable]:
         """
         return self._dim_lengths
 
-    def shape_from_dims(self, dims):
+    def symbolic_shape_from_dims(self, dims):
         shape = []
         if len(set(dims)) != len(dims):
             raise ValueError("Can not contain the same dimension name twice.")

pymc/util.py

@@ -33,44 +33,41 @@
 
 from pymc.exceptions import BlockModelAccessError
 
-# User-facing coordinate types
 CoordValue: TypeAlias = Sequence[Hashable] | np.ndarray | None
+# User-provided values for a single coordinate dimension.
+
 Coords: TypeAlias = Mapping[str, CoordValue]
+# Mapping from dimension name to its coordinate values.
 
-# Internal strong coordinate types
 StrongCoordValue: TypeAlias = tuple[Hashable, ...] | None
+# Normalized coordinate values stored internally.
+
 StrongCoords: TypeAlias = Mapping[str, StrongCoordValue]
+# Mapping from dimension name to normalized coordinate values.
 
-# Internal strong dimension/shape types 
 StrongDims: TypeAlias = tuple[str, ...]
+# Tuple of dimension names after validation.
+
 StrongShape: TypeAlias = tuple[int, ...]
+# Fully-resolved numeric shape used internally.
 
-# User-provided shape before processing
 Shape: TypeAlias = int | TensorVariable | Sequence[int | Variable]
+# User-provided shape specification before normalization.
 
-# User-provided dims before processing
 Dims: TypeAlias = str | Sequence[str | None]
+# User-provided dimension names before normalization.
 
-# User-provided dims that may include ellipsis (...)
 DimsWithEllipsis: TypeAlias = str | EllipsisType | Sequence[str | None | EllipsisType]
+# User-provided dimension names that may include ellipsis.
 
-# User-provided size before processing
 Size: TypeAlias = int | TensorVariable | Sequence[int | Variable]
+# User-provided size specification before normalization.
 
-# Strong / normalized versions used internally
 StrongDimsWithEllipsis: TypeAlias = Sequence[str | EllipsisType]
-StrongSize: TypeAlias = TensorVariable | tuple[int | Variable, ...]
-"""
-Type alias groups:
+# Normalized dimension names that may include ellipsis.
 
-- User-facing types (Coords, CoordValue, Shape, Dims, Size) represent the flexible inputs
-  accepted from users when defining models.
-
-- Strong/internal types (StrongCoords, StrongDims, StrongShape, StrongSize, etc.) represent
-  normalized, fully-validated forms used internally after processing.
-
-These distinctions allow looser user input while keeping strict internal consistency.
-"""
+StrongSize: TypeAlias = TensorVariable | tuple[int | Variable, ...]
+# Normalized symbolic size used internally.
 
 
 class _UnsetType: