Add exported package files and cleanup

This commit includes:
- Exported tinytorch package files from nbdev (autograd, losses, optimizers, training, etc.)
- Updated activations.py and layers.py with __call__ methods
- New module exports: attention, spatial, tokenization, transformer, etc.
- Removed old _modidx.py file
- Cleanup of duplicate milestone directories

These are the generated package files that correspond to the source modules
we've been developing. Students will import from these when using TinyTorch.

tinytorch/optimization/acceleration.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/16_acceleration/acceleration_dev.ipynb.
+
+# %% auto 0
+__all__ = []
+
+# %% ../../modules/source/16_acceleration/acceleration_dev.ipynb 0
+#| default_exp optimization.acceleration
+#| export

tinytorch/optimization/compression.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/18_compression/compression_dev.ipynb.
+
+# %% auto 0
+__all__ = ['Tensor', 'Linear', 'Sequential']
+
+# %% ../../modules/source/18_compression/compression_dev.ipynb 1
+import numpy as np
+import copy
+from typing import List, Dict, Any, Tuple, Optional
+import time
+
+# Import from previous modules
+# Note: In the full package, these would be imports like:
+# from tinytorch.core.tensor import Tensor
+# from tinytorch.core.layers import Linear
+# For development, we'll create minimal implementations
+
+class Tensor:
+    """Minimal Tensor class for compression development - imports from Module 01 in practice."""
+    def __init__(self, data, requires_grad=False):
+        self.data = np.array(data)
+        self.shape = self.data.shape
+        self.size = self.data.size
+        self.requires_grad = requires_grad
+        self.grad = None
+
+    def __add__(self, other):
+        if isinstance(other, Tensor):
+            return Tensor(self.data + other.data)
+        return Tensor(self.data + other)
+
+    def __mul__(self, other):
+        if isinstance(other, Tensor):
+            return Tensor(self.data * other.data)
+        return Tensor(self.data * other)
+
+    def matmul(self, other):
+        return Tensor(np.dot(self.data, other.data))
+
+    def abs(self):
+        return Tensor(np.abs(self.data))
+
+    def sum(self, axis=None):
+        return Tensor(self.data.sum(axis=axis))
+
+    def __repr__(self):
+        return f"Tensor(shape={self.shape})"
+
+class Linear:
+    """Minimal Linear layer for compression development - imports from Module 03 in practice."""
+    def __init__(self, in_features, out_features, bias=True):
+        self.in_features = in_features
+        self.out_features = out_features
+        # Initialize with He initialization
+        self.weight = Tensor(np.random.randn(in_features, out_features) * np.sqrt(2.0 / in_features))
+        self.bias = Tensor(np.zeros(out_features)) if bias else None
+
+    def forward(self, x):
+        output = x.matmul(self.weight)
+        if self.bias is not None:
+            output = output + self.bias
+        return output
+
+    def parameters(self):
+        params = [self.weight]
+        if self.bias is not None:
+            params.append(self.bias)
+        return params
+
+class Sequential:
+    """Minimal Sequential container for model compression."""
+    def __init__(self, *layers):
+        self.layers = list(layers)
+
+    def forward(self, x):
+        for layer in self.layers:
+            x = layer.forward(x)
+        return x
+
+    def parameters(self):
+        params = []
+        for layer in self.layers:
+            if hasattr(layer, 'parameters'):
+                params.extend(layer.parameters())
+        return params

tinytorch/optimization/quantization.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/17_quantization/quantization_dev.ipynb.
+
+# %% auto 0
+__all__ = []
+
+# %% ../../modules/source/17_quantization/quantization_dev.ipynb 0
+#| default_exp optimization.quantization
+#| export