Use function-scoped new_dims to handle type hint varying throughout f…

…unction

We don't want to allow the user to pass a `dims=[None, None]` to our function, but current behaviour
set `dims=[None] * N` at the end of `determine_coords`.

To handle this, I created a `new_dims` with a larger type scope which matches
the return type of `dims` in `determine_coords`.

Then I did the same within def Data to support this new type hint.

pymc/data.py

@@ -218,9 +218,9 @@ def Minibatch(variable: TensorVariable, *variables: TensorVariable, batch_size:
 def determine_coords(
     model,
     value: pd.DataFrame | pd.Series | xr.DataArray,
-    dims: Sequence[str | None] | None = None,
+    dims: Sequence[str] | None = None,
     coords: dict[str, Sequence | np.ndarray] | None = None,
-) -> tuple[dict[str, Sequence | np.ndarray], Sequence[str | None]]:
+) -> tuple[dict[str, Sequence | np.ndarray], Sequence[str] | Sequence[None]]:
     """Determines coordinate values from data or the model (via ``dims``)."""
     if coords is None:
         coords = {}
@@ -265,9 +265,10 @@ def determine_coords(
 
     if dims is None:
         # TODO: Also determine dim names from the index
-        dims = [None] * np.ndim(value)
-
-    return coords, dims
+        new_dims: Sequence[str] | Sequence[None] = [None] * np.ndim(value)
+    else:
+        new_dims = dims
+    return coords, new_dims
 
 
 def ConstantData(
@@ -363,7 +364,7 @@ def Data(
         The name for this variable.
     value : array_like or pandas.Series, pandas.Dataframe
         A value to associate with this variable.
-    dims : str or tuple of str, optional
+    dims : str, tuple of str or tuple of None, optional
         Dimension names of the random variables (as opposed to the shapes of these
         random variables). Use this when ``value`` is a pandas Series or DataFrame. The
         ``dims`` will then be the name of the Series / DataFrame's columns. See ArviZ
@@ -448,13 +449,16 @@ def Data(
             expected=x.ndim,
         )
 
+    new_dims: Sequence[str] | Sequence[None] | None
     if infer_dims_and_coords:
-        coords, dims = determine_coords(model, value, dims)
+        coords, new_dims = determine_coords(model, value, dims)
+    else:
+        new_dims = dims
 
-    if dims:
+    if new_dims:
         xshape = x.shape
         # Register new dimension lengths
-        for d, dname in enumerate(dims):
+        for d, dname in enumerate(new_dims):
             if dname not in model.dim_lengths:
                 model.add_coord(
                     name=dname,
@@ -464,6 +468,6 @@ def Data(
                     length=xshape[d],
                 )
 
-    model.register_data_var(x, dims=dims)
+    model.register_data_var(x, dims=new_dims)
 
     return x