Docs: condition shape constraints on isotropic/anisotropic mode

Author: Talhax55z

monai/networks/nets/segresnet_ds.py

@@ -255,19 +255,23 @@ class SegResNetDS(nn.Module):
                     Otherwise, by default, the kernel size and downsampling is always isotropic.
 
                     Spatial shape constraints:
-    The input spatial dimensions must be divisible by ``2 ** (len(blocks_down) - 1)``.
-    With the default ``blocks_down=(1, 2, 2, 4)`` (4 levels), each spatial dimension
-    must be divisible by 8.
+        If ``resolution is None`` (isotropic downsampling), each input spatial dimension
+        must be divisible by ``2 ** (len(blocks_down) - 1)``.
+        With the default ``blocks_down=(1, 2, 2, 4)`` (4 levels), each spatial dimension
+        must be divisible by 8.
 
-    Use :py:meth:`shape_factor` to query the required divisors for a given configuration,
-    and :py:meth:`is_valid_shape` to check whether a specific input tensor satisfies them.
+        If ``resolution`` is provided, divisibility can differ by dimension based on
+        anisotropic scales; use :py:meth:`shape_factor` for the exact required factors.
 
-    Example::
+        Use :py:meth:`shape_factor` to query the required divisors for a given configuration,
+        and :py:meth:`is_valid_shape` to check whether a specific input tensor satisfies them.
 
-        model = SegResNetDS(spatial_dims=3, blocks_down=(1, 2, 2, 4))
-        print(model.shape_factor())  # [8, 8, 8]
-        # Valid input: shape (1, 1, 128, 128, 128) -- all dims divisible by 8
-        # Invalid input: shape (1, 1, 100, 100, 100) -- 100 is not divisible by 8
+        Example::
+
+            model = SegResNetDS(spatial_dims=3, blocks_down=(1, 2, 2, 4))
+            print(model.shape_factor())  # [8, 8, 8]
+            # Valid input: shape (1, 1, 128, 128, 128) -- all dims divisible by 8
+            # Invalid input: shape (1, 1, 100, 100, 100) -- 100 is not divisible by 8
 
     """