feat: sampling layer w/ forward method + abstract autoencoder

layers.py

@@ -38,6 +38,23 @@ class NNLayer:
         return ret
 
 
+class SamplingLayer:
+    def __init__(self,
+                 in_size: int,
+                 lr: float,
+                 activation_func: ActivationFunc):
+        self.W_mean = np.random.uniform(-0.1, 0.1, (in_size, in_size))
+        self.W_variance = np.random.uniform(-0.1, 0.1, (in_size, in_size))
+
+    def forward(self, v) -> np.ndarray:
+        mean = self.W_mean @ v
+        variance = self.W_variance @ v
+        return np.random.normal(mean, variance)
+
+    def backprop(self, error: np.ndarray) -> np.ndarray:
+        pass
+
+
 class DeepNNLayer:
     def __init__(self,
                  layers: list[int],