refactor: Standardize imports across modules 10-17 to match 01-09

Enforce consistent import pattern across all modules:
- Direct imports from tinytorch.core.* (no fallbacks)
- Remove all sys.path.append manipulations
- Remove try/except import fallbacks
- Remove mock/dummy class fallbacks

Fixed modules:
- Module 10 (tokenization): Removed try/except fallback
- Module 12 (attention): Removed sys.path.append for tensor/layers
- Module 15 (profiling): Removed sys.path + mock Tensor/Linear/Conv2d
- Module 16 (acceleration): Removed hardcoded path + importlib + mock Tensor
- Module 17 (quantization): Removed sys.path + disabled fallback block

All modules now follow the same pattern as modules 01-09:
  from tinytorch.core.tensor import Tensor
  from tinytorch.core.layers import Linear
  # etc.

No development fallbacks - assume tinytorch package is installed.

modules/source/15_profiling/profiling_dev.py

@@ -67,27 +67,9 @@ from collections import defaultdict
 import gc
 
 # Import our TinyTorch components for profiling
-import sys
-import os
-sys.path.append(os.path.join(os.path.dirname(__file__), '..', '01_tensor'))
-sys.path.append(os.path.join(os.path.dirname(__file__), '..', '03_layers'))
-sys.path.append(os.path.join(os.path.dirname(__file__), '..', '09_spatial'))
-
-# For testing purposes - in real package these would be proper imports
-try:
-    from tensor_dev import Tensor
-    from layers_dev import Linear, Sequential
-    from spatial_dev import Conv2d
-except ImportError:
-    # Fallback - create minimal implementations for testing
-    class Tensor:
-        def __init__(self, data):
-            self.data = np.array(data)
-            self.shape = self.data.shape
-        def __mul__(self, other):
-            return Tensor(self.data * other.data)
-        def sum(self):
-            return Tensor(np.sum(self.data))
+from tinytorch.core.tensor import Tensor
+from tinytorch.core.layers import Linear
+from tinytorch.core.spatial import Conv2d
 
 # %% [markdown]
 """