feat: implement selective exports for modules 09-11

- 09_spatial: Export Conv2d, MaxPool2d, AvgPool2d only
- 10_tokenization: Export Tokenizer, CharTokenizer, BPETokenizer only
- 11_embeddings: Export Embedding, PositionalEncoding only

Continues professional selective export pattern. Clean public APIs,
development utilities remain in development environment.

modules/source/10_tokenization/tokenization_dev.py

@@ -195,6 +195,7 @@ This ensures consistent behavior across different tokenization strategies.
 """
 
 # %% nbgrader={"grade": false, "grade_id": "base-tokenizer", "solution": true}
+#| export
 class Tokenizer:
     """
     Base tokenizer class providing the interface for all tokenizers.
@@ -303,6 +304,7 @@ Result: "hello"
 """
 
 # %% nbgrader={"grade": false, "grade_id": "char-tokenizer", "solution": true}
+#| export
 class CharTokenizer(Tokenizer):
     """
     Character-level tokenizer that treats each character as a separate token.
@@ -513,6 +515,7 @@ BPE discovers natural word boundaries and common patterns automatically!
 """
 
 # %% nbgrader={"grade": false, "grade_id": "bpe-tokenizer", "solution": true}
+#| export
 class BPETokenizer(Tokenizer):
     """
     Byte Pair Encoding (BPE) tokenizer that learns subword units.

modules/source/11_embeddings/embeddings_dev.py

@@ -201,6 +201,7 @@ Now let's build the core embedding layer that performs efficient token-to-vector
 """
 
 # %% nbgrader={"grade": false, "grade_id": "embedding-class", "solution": true}
+#| export
 class Embedding:
     """
     Learnable embedding layer that maps token indices to dense vectors.
@@ -402,6 +403,7 @@ Let's build trainable positional embeddings that can learn position-specific pat
 """
 
 # %% nbgrader={"grade": false, "grade_id": "positional-encoding", "solution": true}
+#| export
 class PositionalEncoding:
     """
     Learnable positional encoding layer.