diff --git a/.vscode/launch.json b/.vscode/launch.json
index c0ab2348..0be7bc2a 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -146,7 +146,7 @@
 
         {
             "name": "AgingEvolution",
-            "type": "python",
+            "type": "debugpy",
             "request": "launch",
             "module": "hannah.tools.train",
             "justMyCode": false,
@@ -162,12 +162,26 @@
                 "trainer.overfit_batches=1",
                 "nas.n_jobs=1",
                 "nas.budget=100",
-                "nas.total_candidates=50",
-                "nas.num_selected_candidates=20",
-                "nas.sampler.population_size=20",
-                // "nas.predictor.model.input_feature_size=31",
+                "nas.total_candidates=5",
+                "nas.num_selected_candidates=2",
+                "nas.sampler.population_size=2",
+                // "nas.predictor.model.input_feature_size=35",
                 "module.num_workers=8",
-                "experiment_id=test_constraint_config",
+                "experiment_id=test_merge",
+                "fx_mac_summary=True",
+                // "~nas.predictor",
+                "~normalizer"
+            ]
+        },
+        {
+            "name": "ChainedLinear",
+            "type": "python",
+            "request": "launch",
+            "module": "hannah.tools.train",
+            "justMyCode": false,
+            "cwd": "${workspaceFolder}/experiments/kws",
+            "args": [
+                "+experiment=ae_nas"
                 "fx_mac_summary=True",
                 // "~nas.predictor",
                 "~normalizer"
diff --git a/doc/nas/search_spaces.md b/doc/nas/search_spaces.md
index 80194e92..74584873 100644
--- a/doc/nas/search_spaces.md
+++ b/doc/nas/search_spaces.md
@@ -20,6 +20,16 @@ from hannah.nas.functional_operators.operators import Conv2d
 
 ## Basic Building Blocks
 
+### Search Space Wrapper
+To define the beginning and end of a search space, the definition has to be enclosed in a function returning the (last node of the) search space graph. 
+This function must use the `@search_space` decorator to indicate that this is the main search space enclosing function. 
+
+
+
+```python
+from hannah.nas.functional_operators.op import search_space
+```
+
 ### Ops & Tensors
 
 **Op** nodes represent the operators used in the networks of the search space. Their basic syntax is
@@ -38,18 +48,22 @@ defines attributes that the data has at this point in the graph (e.g., shape, ax
 from hannah.nas.functional_operators.operators import Conv2d
 from hannah.nas.functional_operators.op import Tensor
 
-input = Tensor(name='input', shape=(1, 3, 32, 32), axis=("N", "C", "H", "W"))
-weight = Tensor(name='weight', shape=(32, 3, 1, 1), axis=("O", "I", "kH", "kW"))
+@search_space
+def simple_search_space():
+    input = Tensor(name='input', shape=(1, 3, 32, 32), axis=("N", "C", "H", "W"))
+    weight = Tensor(name='weight', shape=(32, 3, 1, 1), axis=("O", "I", "kH", "kW"))
 
-conv = Conv2d(stride=2, dilation=1)   # Define operator and parametrization
-graph = conv(input, weight)           # Define/create/extend graph
+    conv = Conv2d(stride=2, dilation=1)   # Define operator and parametrization
+    graph = conv(input, weight)           # Define/create/extend graph
+    return graph
+graph = simple_search_space()
 graph
 ```
 
 
 
 
-    Conv2d(Conv2d_0)
+    Conv2d(simple_search_space_0.Conv2d_0)
 
 
 
@@ -76,19 +90,23 @@ To build a search space it is not sufficient to feed scalar values to operator p
 ```python
 from hannah.nas.parameters.parameters import CategoricalParameter, IntScalarParameter
 
-input = Tensor(name='input', shape=(1, 3, 32, 32), axis=("N", "C", "H", "W"))
-weight = Tensor(name='weight', shape=(IntScalarParameter(min=8, max=64, name='out_channels'), 3, 1, 1), axis=("O", "I", "kH", "kW"))
+@search_space
+def simple_parametrized_search_space():
+    input = Tensor(name='input', shape=(1, 3, 32, 32), axis=("N", "C", "H", "W"))
+    weight = Tensor(name='weight', shape=(IntScalarParameter(min=8, max=64, name='out_channels'), 3, 1, 1), axis=("O", "I", "kH", "kW"))
 
-# a search space with stride 1 and stride 2 convolutions
-graph = Conv2d(stride=CategoricalParameter(name='stride', choices=[1, 2]))(input, weight)
+    # a search space with stride 1 and stride 2 convolutions
+    graph = Conv2d(stride=CategoricalParameter(name='stride', choices=[1, 2]))(input, weight)
+    return graph
+graph = simple_parametrized_search_space()
 graph.parametrization(flatten=True)
 ```
 
 
 
 
-    {'Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = Conv2d_0.weight.out_channels, min = 8, max = 64, step_size = 1, current_value = 8),
-     None: CategoricalParameter(rng = Generator(PCG64), name = stride, id = None, choices = [1, 2], current_value = 1)}
+    {'simple_parametrized_search_space_0.Conv2d_0.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = simple_parametrized_search_space_0.Conv2d_0.stride, _registered = True, choices = [1, 2], current_value = 2),
+     'simple_parametrized_search_space_0.Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = simple_parametrized_search_space_0.Conv2d_0.weight.out_channels, _registered = True, min = 8, max = 64, step_size = 1, current_value = 8)}
 
 
 
@@ -97,16 +115,20 @@ encoding properties of the search space symbolically. One common use-case is sym
 
 
 ```python
-in_channel = 3 
+in_channel = 3
 kernel_size = 1
 input = Tensor(name='input',
                shape=(1, in_channel, 32, 32),
                axis=('N', 'C', 'H', 'W'))
 
-weight_0 = Tensor(name='weight', 
-                  shape=(IntScalarParameter(min=8, max=64, name='out_channels'), in_channel, kernel_size, kernel_size), 
-                  axis=("O", "I", "kH", "kW"))
-conv_0 = Conv2d(stride=CategoricalParameter(name='stride', choices=[1, 2]))(input, weight_0)
+@search_space
+def simple_search_space(input):
+    weight_0 = Tensor(name='weight',
+                      shape=(IntScalarParameter(min=8, max=64, name='out_channels'), in_channel, kernel_size, kernel_size),
+                      axis=("O", "I", "kH", "kW"))
+    conv_0 = Conv2d(stride=CategoricalParameter(name='stride', choices=[1, 2]))(input, weight_0)
+    return conv_0
+out = simple_search_space(input)
 ```
 
 How can we know the output shape of `conv_0`, e.g., to put it into the weight tensor of a following convolution, without knowing what value 
@@ -117,13 +139,13 @@ are then only evaluated at sampling and during the forward.
 
 ```python
 print("Input shape: ", input.shape())
-print("Weight shape: ", weight_0.shape())
-print("Convolution output shape:", conv_0.shape())
+print("Weight shape: ", out.operands[1].shape())
+print("Convolution output shape:", out.shape())
 ```
 
     Input shape:  (1, 3, 32, 32)
-    Weight shape:  (IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = Conv2d_0.weight.out_channels, min = 8, max = 64, step_size = 1, current_value = 8), 3, 1, 1)
-    Convolution output shape: (1, IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = Conv2d_0.weight.out_channels, min = 8, max = 64, step_size = 1, current_value = 8), <hannah.nas.expressions.arithmetic.Floor object at 0x7ff93a6ce8c0>, <hannah.nas.expressions.arithmetic.Floor object at 0x7ff93a6ce7a0>)
+    Weight shape:  (IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = simple_search_space_0.Conv2d_0.weight.out_channels, _registered = True, min = 8, max = 64, step_size = 1, current_value = 8), 3, 1, 1)
+    Convolution output shape: (1, IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = simple_search_space_0.Conv2d_0.weight.out_channels, _registered = True, min = 8, max = 64, step_size = 1, current_value = 8), <hannah.nas.expressions.arithmetic.Floor object at 0x7fcaff5b3d60>, <hannah.nas.expressions.arithmetic.Floor object at 0x7fcaff5b2560>)
 
 
 The `lazy` keyword can be used to evaluate values which *might* be parameters (but could also be `int` or else).
@@ -134,8 +156,8 @@ from hannah.nas.functional_operators.lazy import lazy
 
 
 print("Input shape: ", [lazy(i) for i in input.shape()])
-print("Weight shape: ", [lazy(i) for i in weight_0.shape()])
-print("Convolution output shape:", [lazy(i) for i in conv_0.shape()])
+print("Weight shape: ", [lazy(i) for i in out.operands[1].shape()])
+print("Convolution output shape:", [lazy(i) for i in out.shape()])
 ```
 
     Input shape:  [1, 3, 32, 32]
@@ -153,33 +175,38 @@ As seen in the simple examples above, we can chain op and tensor nodes together
 ```python
 from hannah.nas.functional_operators.operators import Relu
 
-input = Tensor(name='input',
-               shape=(1, 3, 32, 32),
-               axis=('N', 'C', 'H', 'W'))
 
-weight_0 = Tensor(name='weight', shape=(IntScalarParameter(min=8, max=64, name='out_channels'), 3, 1, 1), axis=("O", "I", "kH", "kW"))
+@search_space
+def simple_search_space():
+    input = Tensor(name='input',
+                   shape=(1, 3, 32, 32),
+                   axis=('N', 'C', 'H', 'W'))
 
-conv_0 = Conv2d(stride=CategoricalParameter(name='stride', choices=[1, 2]))(input, weight_0)
-relu_0 = Relu()(conv_0)
+    weight_0 = Tensor(name='weight', shape=(IntScalarParameter(min=8, max=64, name='out_channels'), 3, 1, 1), axis=("O", "I", "kH", "kW"))
 
-weight_1 = Tensor(name='weight', shape=(IntScalarParameter(min=32, max=64, name='out_channels'), conv_0.shape()[1], 3, 3), axis=("O", "I", "kH", "kW"))
-conv_1 = Conv2d(stride=CategoricalParameter(name='stride', choices=[1, 2]))(relu_0, weight_1)
-relu_1 = Relu()(conv_1)
+    conv_0 = Conv2d(stride=CategoricalParameter(name='stride', choices=[1, 2]))(input, weight_0)
+    relu_0 = Relu()(conv_0)
+
+    weight_1 = Tensor(name='weight', shape=(IntScalarParameter(min=32, max=64, name='out_channels'), conv_0.shape()[1], 3, 3), axis=("O", "I", "kH", "kW"))
+    conv_1 = Conv2d(stride=CategoricalParameter(name='stride', choices=[1, 2]))(relu_0, weight_1)
+    relu_1 = Relu()(conv_1)
+    return relu_1
+out = simple_search_space()
 
 ```
 
 
 ```python
-relu_1.parametrization(flatten=True)
+out.parametrization(flatten=True)
 ```
 
 
 
 
-    {'Conv2d_1.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = Conv2d_1.weight.out_channels, min = 32, max = 64, step_size = 1, current_value = 32),
-     'Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = Conv2d_0.weight.out_channels, min = 8, max = 64, step_size = 1, current_value = 8),
-     'Conv2d_0.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = Conv2d_0.stride, choices = [1, 2], current_value = 2),
-     'Conv2d_1.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = Conv2d_1.stride, choices = [1, 2], current_value = 2)}
+    {'simple_search_space_0.Conv2d_1.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = simple_search_space_0.Conv2d_1.stride, _registered = True, choices = [1, 2], current_value = 2),
+     'simple_search_space_0.Conv2d_1.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = simple_search_space_0.Conv2d_1.weight.out_channels, _registered = True, min = 32, max = 64, step_size = 1, current_value = 32),
+     'simple_search_space_0.Conv2d_0.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = simple_search_space_0.Conv2d_0.stride, _registered = True, choices = [1, 2], current_value = 2),
+     'simple_search_space_0.Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = simple_search_space_0.Conv2d_0.weight.out_channels, _registered = True, min = 8, max = 64, step_size = 1, current_value = 8)}
 
 
 
@@ -190,33 +217,33 @@ With helper functions like `get_nodes` one can iterate through all graph nodes.
 ```python
 from hannah.nas.functional_operators.op import get_nodes
 
-print("Relu Operands: ", relu_1.operands)
-print("Conv Users: ", relu_1.operands[0].users)
+print("Relu Operands: ", out.operands)
+print("Conv Users: ", out.operands[0].users)
 
 print("\nNodes:")
-for node in get_nodes(relu_1):
+for node in get_nodes(out):
     print('Node:', node)
     print('\tOperands: ', node.operands)
 
 ```
 
-    Relu Operands:  [Conv2d(Conv2d_1)]
-    Conv Users:  [Relu(Relu_1)]
+    Relu Operands:  [Conv2d(simple_search_space_0.Conv2d_1)]
+    Conv Users:  [Relu(simple_search_space_0.Relu_1)]
     
     Nodes:
-    Node: Relu(Relu_1)
-    	Operands:  [Conv2d(Conv2d_1)]
-    Node: Conv2d(Conv2d_1)
-    	Operands:  [Relu(Relu_0), Tensor(Conv2d_1.weight)]
-    Node: Tensor(Conv2d_1.weight)
+    Node: Relu(simple_search_space_0.Relu_1)
+    	Operands:  [Conv2d(simple_search_space_0.Conv2d_1)]
+    Node: Conv2d(simple_search_space_0.Conv2d_1)
+    	Operands:  [Relu(simple_search_space_0.Relu_0), Tensor(simple_search_space_0.Conv2d_1.weight)]
+    Node: Tensor(simple_search_space_0.Conv2d_1.weight)
     	Operands:  []
-    Node: Relu(Relu_0)
-    	Operands:  [Conv2d(Conv2d_0)]
-    Node: Conv2d(Conv2d_0)
-    	Operands:  [Tensor(input), Tensor(Conv2d_0.weight)]
-    Node: Tensor(Conv2d_0.weight)
+    Node: Relu(simple_search_space_0.Relu_0)
+    	Operands:  [Conv2d(simple_search_space_0.Conv2d_0)]
+    Node: Conv2d(simple_search_space_0.Conv2d_0)
+    	Operands:  [Tensor(simple_search_space_0.input), Tensor(simple_search_space_0.Conv2d_0.weight)]
+    Node: Tensor(simple_search_space_0.Conv2d_0.weight)
     	Operands:  []
-    Node: Tensor(input)
+    Node: Tensor(simple_search_space_0.input)
     	Operands:  []
 
 
@@ -243,23 +270,27 @@ def conv_relu(input, kernel_size, out_channels, stride):
 input = Tensor(name='input',
                shape=(1, 3, 32, 32),
                axis=('N', 'C', 'H', 'W'))
+@search_space
+def space(input):
+    kernel_size = CategoricalParameter(name="kernel_size", choices=[1, 3, 5])
+    stride = CategoricalParameter(name="stride", choices=[1, 2])
+    out_channels = IntScalarParameter(name="out_channels", min=8, max=64)
+    net = conv_relu(input, kernel_size=kernel_size, out_channels=out_channels, stride=stride)
+    net = conv_relu(net, kernel_size=kernel_size, out_channels=out_channels, stride=stride)
+    return net
 
-kernel_size = CategoricalParameter(name="kernel_size", choices=[1, 3, 5])
-stride = CategoricalParameter(name="stride", choices=[1, 2])
-out_channels = IntScalarParameter(name="out_channels", min=8, max=64)
-net = conv_relu(input, kernel_size=kernel_size, out_channels=out_channels, stride=stride)
-net = conv_relu(net, kernel_size=kernel_size, out_channels=out_channels, stride=stride)
+net = space(input)
 
 for n in get_nodes(net):
     print(n)
 ```
 
-    Relu(Relu_1)
-    Conv2d(Conv2d_1)
-    Tensor(Conv2d_1.weight)
-    Relu(Relu_0)
-    Conv2d(Conv2d_0)
-    Tensor(Conv2d_0.weight)
+    Relu(space_0.Relu_1)
+    Conv2d(space_0.Conv2d_1)
+    Tensor(space_0.Conv2d_1.weight)
+    Relu(space_0.Relu_0)
+    Conv2d(space_0.Conv2d_0)
+    Tensor(space_0.Conv2d_0.weight)
     Tensor(input)
 
 
@@ -271,9 +302,9 @@ net.parametrization(flatten=True)
 
 
 
-    {'Conv2d_0.weight.kernel_size': CategoricalParameter(rng = Generator(PCG64), name = kernel_size, id = Conv2d_0.weight.kernel_size, choices = [1, 3, 5], current_value = 5),
-     'Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = Conv2d_0.weight.out_channels, min = 8, max = 64, step_size = 1, current_value = 8),
-     'Conv2d_0.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = Conv2d_0.stride, choices = [1, 2], current_value = 1)}
+    {'space_0.Conv2d_0.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = space_0.Conv2d_0.stride, _registered = True, choices = [1, 2], current_value = 1),
+     'space_0.Conv2d_0.weight.kernel_size': CategoricalParameter(rng = Generator(PCG64), name = kernel_size, id = space_0.Conv2d_0.weight.kernel_size, _registered = True, choices = [1, 3, 5], current_value = 1),
+     'space_0.Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = space_0.Conv2d_0.weight.out_channels, _registered = True, min = 8, max = 64, step_size = 1, current_value = 8)}
 
 
 
@@ -284,12 +315,15 @@ Note, how there is just one set of parameters. If defined this way, both blocks
 input = Tensor(name='input',
                shape=(1, 3, 32, 32),
                axis=('N', 'C', 'H', 'W'))
-
-kernel_size = CategoricalParameter(name="kernel_size", choices=[1, 3, 5])
-stride = CategoricalParameter(name="stride", choices=[1, 2])
-out_channels = IntScalarParameter(name="out_channels", min=8, max=64)
-net = conv_relu(input, kernel_size=kernel_size.new(), out_channels=out_channels.new(), stride=stride.new())
-net = conv_relu(net, kernel_size=kernel_size.new(), out_channels=out_channels.new(), stride=stride.new())
+@search_space
+def space(input):
+    kernel_size = CategoricalParameter(name="kernel_size", choices=[1, 3, 5])
+    stride = CategoricalParameter(name="stride", choices=[1, 2])
+    out_channels = IntScalarParameter(name="out_channels", min=8, max=64)
+    net = conv_relu(input, kernel_size=kernel_size.new(), out_channels=out_channels.new(), stride=stride.new())
+    net = conv_relu(net, kernel_size=kernel_size.new(), out_channels=out_channels.new(), stride=stride.new())
+    return net
+net = space(input)
 
 net.parametrization(flatten=True)
 ```
@@ -297,12 +331,12 @@ net.parametrization(flatten=True)
 
 
 
-    {'Conv2d_1.weight.kernel_size': CategoricalParameter(rng = Generator(PCG64), name = kernel_size, id = Conv2d_1.weight.kernel_size, choices = [1, 3, 5], current_value = 3),
-     'Conv2d_1.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = Conv2d_1.weight.out_channels, min = 8, max = 64, step_size = 1, current_value = 8),
-     'Conv2d_0.weight.kernel_size': CategoricalParameter(rng = Generator(PCG64), name = kernel_size, id = Conv2d_0.weight.kernel_size, choices = [1, 3, 5], current_value = 3),
-     'Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = Conv2d_0.weight.out_channels, min = 8, max = 64, step_size = 1, current_value = 8),
-     'Conv2d_0.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = Conv2d_0.stride, choices = [1, 2], current_value = 2),
-     'Conv2d_1.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = Conv2d_1.stride, choices = [1, 2], current_value = 2)}
+    {'space_0.Conv2d_1.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = space_0.Conv2d_1.stride, _registered = True, choices = [1, 2], current_value = 2),
+     'space_0.Conv2d_1.weight.kernel_size': CategoricalParameter(rng = Generator(PCG64), name = kernel_size, id = space_0.Conv2d_1.weight.kernel_size, _registered = True, choices = [1, 3, 5], current_value = 5),
+     'space_0.Conv2d_1.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = space_0.Conv2d_1.weight.out_channels, _registered = True, min = 8, max = 64, step_size = 1, current_value = 8),
+     'space_0.Conv2d_0.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = space_0.Conv2d_0.stride, _registered = True, choices = [1, 2], current_value = 2),
+     'space_0.Conv2d_0.weight.kernel_size': CategoricalParameter(rng = Generator(PCG64), name = kernel_size, id = space_0.Conv2d_0.weight.kernel_size, _registered = True, choices = [1, 3, 5], current_value = 5),
+     'space_0.Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = space_0.Conv2d_0.weight.out_channels, _registered = True, min = 8, max = 64, step_size = 1, current_value = 8)}
 
 
 
@@ -322,31 +356,35 @@ def block(input):
 input = Tensor(name='input',
                shape=(1, 3, 32, 32),
                axis=('N', 'C', 'H', 'W'))
-net = block(input)
-net = block(net)
+@search_space
+def space(input):
+    net = block(input)
+    net = block(net)
+    return net
+net = space(input)
 
 for n in get_nodes(net):
     print(n)
 ```
 
-    Relu(Relu_5)
-    Conv2d(Conv2d_5)
-    Tensor(Conv2d_5.weight)
-    Relu(Relu_4)
-    Conv2d(Conv2d_4)
-    Tensor(Conv2d_4.weight)
-    Relu(Relu_3)
-    Conv2d(Conv2d_3)
-    Tensor(Conv2d_3.weight)
-    Relu(Relu_2)
-    Conv2d(Conv2d_2)
-    Tensor(Conv2d_2.weight)
-    Relu(Relu_1)
-    Conv2d(Conv2d_1)
-    Tensor(Conv2d_1.weight)
-    Relu(Relu_0)
-    Conv2d(Conv2d_0)
-    Tensor(Conv2d_0.weight)
+    Relu(space_0.Relu_5)
+    Conv2d(space_0.Conv2d_5)
+    Tensor(space_0.Conv2d_5.weight)
+    Relu(space_0.Relu_4)
+    Conv2d(space_0.Conv2d_4)
+    Tensor(space_0.Conv2d_4.weight)
+    Relu(space_0.Relu_3)
+    Conv2d(space_0.Conv2d_3)
+    Tensor(space_0.Conv2d_3.weight)
+    Relu(space_0.Relu_2)
+    Conv2d(space_0.Conv2d_2)
+    Tensor(space_0.Conv2d_2.weight)
+    Relu(space_0.Relu_1)
+    Conv2d(space_0.Conv2d_1)
+    Tensor(space_0.Conv2d_1.weight)
+    Relu(space_0.Relu_0)
+    Conv2d(space_0.Conv2d_0)
+    Tensor(space_0.Conv2d_0.weight)
     Tensor(input)
 
 
@@ -381,34 +419,39 @@ def block(input):
     net = conv_relu(net, kernel_size=kernel_size.new(), out_channels=out_channels.new(), stride=stride.new())
     return net
 
+
 input = Tensor(name='input',
                shape=(1, 3, 32, 32),
                axis=('N', 'C', 'H', 'W'))
-net = block(input)
-net = block(net)
+@search_space
+def space(input):
+    net = block(input)
+    net = block(net)
+    return net
+net = space(input)
 
 for n in get_nodes(net):
     print(n)
 ```
 
-    Relu(block_1.conv_relu_2.Relu_0)
-    Conv2d(block_1.conv_relu_2.Conv2d_0)
-    Tensor(block_1.conv_relu_2.Conv2d_0.weight)
-    Relu(block_1.conv_relu_1.Relu_0)
-    Conv2d(block_1.conv_relu_1.Conv2d_0)
-    Tensor(block_1.conv_relu_1.Conv2d_0.weight)
-    Relu(block_1.conv_relu_0.Relu_0)
-    Conv2d(block_1.conv_relu_0.Conv2d_0)
-    Tensor(block_1.conv_relu_0.Conv2d_0.weight)
-    Relu(block_0.conv_relu_2.Relu_0)
-    Conv2d(block_0.conv_relu_2.Conv2d_0)
-    Tensor(block_0.conv_relu_2.Conv2d_0.weight)
-    Relu(block_0.conv_relu_1.Relu_0)
-    Conv2d(block_0.conv_relu_1.Conv2d_0)
-    Tensor(block_0.conv_relu_1.Conv2d_0.weight)
-    Relu(block_0.conv_relu_0.Relu_0)
-    Conv2d(block_0.conv_relu_0.Conv2d_0)
-    Tensor(block_0.conv_relu_0.Conv2d_0.weight)
+    Relu(space_0.block_1.conv_relu_2.Relu_0)
+    Conv2d(space_0.block_1.conv_relu_2.Conv2d_0)
+    Tensor(space_0.block_1.conv_relu_2.Conv2d_0.weight)
+    Relu(space_0.block_1.conv_relu_1.Relu_0)
+    Conv2d(space_0.block_1.conv_relu_1.Conv2d_0)
+    Tensor(space_0.block_1.conv_relu_1.Conv2d_0.weight)
+    Relu(space_0.block_1.conv_relu_0.Relu_0)
+    Conv2d(space_0.block_1.conv_relu_0.Conv2d_0)
+    Tensor(space_0.block_1.conv_relu_0.Conv2d_0.weight)
+    Relu(space_0.block_0.conv_relu_2.Relu_0)
+    Conv2d(space_0.block_0.conv_relu_2.Conv2d_0)
+    Tensor(space_0.block_0.conv_relu_2.Conv2d_0.weight)
+    Relu(space_0.block_0.conv_relu_1.Relu_0)
+    Conv2d(space_0.block_0.conv_relu_1.Conv2d_0)
+    Tensor(space_0.block_0.conv_relu_1.Conv2d_0.weight)
+    Relu(space_0.block_0.conv_relu_0.Relu_0)
+    Conv2d(space_0.block_0.conv_relu_0.Conv2d_0)
+    Tensor(space_0.block_0.conv_relu_0.Conv2d_0.weight)
     Tensor(input)
 
 
@@ -433,14 +476,24 @@ def choice_block(input):
     net = ChoiceOp(identity, optional_conv)(input)
     return net
 
-    
+
 ```
 
 
 ```python
+kernel_size = CategoricalParameter(name="kernel_size", choices=[1, 3, 5])
+stride = CategoricalParameter(name="stride", choices=[1, 2])
+out_channels = IntScalarParameter(name="out_channels", min=8, max=64)
+
+
 input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
-conv = conv_relu(input, out_channels=out_channels.new(), stride=stride.new(), kernel_size=kernel_size.new())
-net = choice_block(conv)
+
+@search_space
+def space(input, out_channels, stride, kernel_size):
+    conv = conv_relu(input, out_channels=out_channels.new(), stride=stride.new(), kernel_size=kernel_size.new())
+    net = choice_block(conv)
+    return net
+net = space(input, out_channels, stride, kernel_size)
 
 net.parametrization(flatten=True)
 
@@ -449,13 +502,13 @@ net.parametrization(flatten=True)
 
 
 
-    {'choice_block_0.ChoiceOp_0.choice': IntScalarParameter(rng = Generator(PCG64), name = choice, id = choice_block_0.ChoiceOp_0.choice, min = 0, max = 1, step_size = 1, current_value = 0),
-     'choice_block_0.conv_relu_1.Conv2d_0.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = choice_block_0.conv_relu_1.Conv2d_0.stride, choices = [1, 2], current_value = 2),
-     'choice_block_0.conv_relu_1.Conv2d_0.weight.kernel_size': CategoricalParameter(rng = Generator(PCG64), name = kernel_size, id = choice_block_0.conv_relu_1.Conv2d_0.weight.kernel_size, choices = [1, 3, 5], current_value = 5),
-     'choice_block_0.conv_relu_1.Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = choice_block_0.conv_relu_1.Conv2d_0.weight.out_channels, min = 4, max = 64, step_size = 1, current_value = 4),
-     'conv_relu_0.Conv2d_0.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = conv_relu_0.Conv2d_0.stride, choices = [1, 2], current_value = 2),
-     'conv_relu_0.Conv2d_0.weight.kernel_size': CategoricalParameter(rng = Generator(PCG64), name = kernel_size, id = conv_relu_0.Conv2d_0.weight.kernel_size, choices = [1, 3, 5], current_value = 3),
-     'conv_relu_0.Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = conv_relu_0.Conv2d_0.weight.out_channels, min = 8, max = 64, step_size = 1, current_value = 8)}
+    {'space_0.choice_block_0.ChoiceOp_0.choice': IntScalarParameter(rng = Generator(PCG64), name = choice, id = space_0.choice_block_0.ChoiceOp_0.choice, _registered = True, min = 0, max = 1, step_size = 1, current_value = 0),
+     'space_0.choice_block_0.conv_relu_0.Conv2d_0.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = space_0.choice_block_0.conv_relu_0.Conv2d_0.stride, _registered = True, choices = [1, 2], current_value = 1),
+     'space_0.choice_block_0.conv_relu_0.Conv2d_0.weight.kernel_size': CategoricalParameter(rng = Generator(PCG64), name = kernel_size, id = space_0.choice_block_0.conv_relu_0.Conv2d_0.weight.kernel_size, _registered = True, choices = [1, 3, 5], current_value = 1),
+     'space_0.choice_block_0.conv_relu_0.Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = space_0.choice_block_0.conv_relu_0.Conv2d_0.weight.out_channels, _registered = True, min = 4, max = 64, step_size = 1, current_value = 4),
+     'space_0.conv_relu_0.Conv2d_0.stride': CategoricalParameter(rng = Generator(PCG64), name = stride, id = space_0.conv_relu_0.Conv2d_0.stride, _registered = True, choices = [1, 2], current_value = 1),
+     'space_0.conv_relu_0.Conv2d_0.weight.kernel_size': CategoricalParameter(rng = Generator(PCG64), name = kernel_size, id = space_0.conv_relu_0.Conv2d_0.weight.kernel_size, _registered = True, choices = [1, 3, 5], current_value = 3),
+     'space_0.conv_relu_0.Conv2d_0.weight.out_channels': IntScalarParameter(rng = Generator(PCG64), name = out_channels, id = space_0.conv_relu_0.Conv2d_0.weight.out_channels, _registered = True, min = 8, max = 64, step_size = 1, current_value = 8)}
 
 
 
@@ -494,9 +547,12 @@ from hannah.nas.functional_operators.executor import BasicExecutor
 input = Tensor(name='input',
                shape=(1, 3, 32, 32),
                axis=('N', 'C', 'H', 'W'))
-net = block(input)
-net = block(net)
-
+@search_space
+def space(input):
+    net = block(input)
+    net = block(net)
+    return net
+net = space(input)
 model = BasicExecutor(net)
 model.initialize()
 
@@ -507,44 +563,49 @@ model.forward(x)
 
 
 
-    tensor([[[[0.0000, 0.0000, 0.0000, 0.0000],
+    tensor([[[[0.2717, 0.0092, 0.1203, 0.1979],
+              [0.0000, 0.2005, 0.0972, 0.0256],
+              [0.1351, 0.1363, 0.0754, 0.1609],
+              [0.0000, 0.1031, 0.0446, 0.2227]],
+    
+             [[0.2462, 0.0013, 0.0224, 0.0534],
+              [0.2030, 0.1310, 0.0000, 0.0404],
+              [0.1303, 0.1276, 0.0634, 0.1498],
+              [0.1786, 0.0298, 0.0085, 0.1301]],
+    
+             [[0.0000, 0.0000, 0.0000, 0.0000],
               [0.0000, 0.0000, 0.0000, 0.0000],
               [0.0000, 0.0000, 0.0000, 0.0000],
               [0.0000, 0.0000, 0.0000, 0.0000]],
     
-             [[0.0255, 0.0000, 0.0000, 0.0000],
-              [0.0000, 0.0152, 0.0000, 0.0000],
-              [0.0000, 0.0000, 0.0898, 0.0000],
-              [0.1132, 0.0894, 0.0094, 0.0138]],
-    
-             [[0.0000, 0.0000, 0.0365, 0.0000],
-              [0.0000, 0.1532, 0.0000, 0.2529],
-              [0.0000, 0.0859, 0.0396, 0.0000],
-              [0.0000, 0.2311, 0.0757, 0.0000]],
-    
-             [[0.0000, 0.1285, 0.1754, 0.0000],
-              [0.1788, 0.1729, 0.1973, 0.1036],
-              [0.1823, 0.2994, 0.2293, 0.2580],
-              [0.0554, 0.2454, 0.1355, 0.3018]],
+             [[0.0000, 0.0021, 0.0000, 0.0000],
+              [0.0000, 0.0000, 0.0232, 0.0000],
+              [0.0000, 0.0000, 0.0000, 0.0000],
+              [0.0000, 0.0000, 0.0011, 0.0000]],
     
-             [[0.0000, 0.0234, 0.0000, 0.0000],
-              [0.0725, 0.0212, 0.0615, 0.0960],
-              [0.1040, 0.0960, 0.1613, 0.0927],
-              [0.1025, 0.0846, 0.0000, 0.0424]],
+             [[0.7481, 0.0018, 0.2029, 0.1693],
+              [0.7117, 0.3248, 0.1578, 0.1085],
+              [0.3086, 0.3926, 0.1606, 0.3065],
+              [0.5410, 0.1157, 0.0583, 0.4534]],
     
-             [[0.0000, 0.0000, 0.0672, 0.0818],
-              [0.0000, 0.1420, 0.0404, 0.0326],
-              [0.0000, 0.0000, 0.0000, 0.1140],
-              [0.0000, 0.1518, 0.1521, 0.2088]],
+             [[0.0000, 0.0000, 0.0705, 0.0628],
+              [0.0000, 0.0000, 0.1682, 0.0000],
+              [0.0000, 0.0000, 0.0000, 0.0000],
+              [0.0000, 0.0381, 0.0255, 0.0000]],
     
-             [[0.0000, 0.0995, 0.1362, 0.0000],
-              [0.0000, 0.1206, 0.0000, 0.0000],
-              [0.0000, 0.1001, 0.0000, 0.0000],
-              [0.0000, 0.0000, 0.0000, 0.0435]],
+             [[0.7549, 0.0092, 0.2340, 0.1351],
+              [0.7965, 0.1582, 0.2039, 0.0925],
+              [0.2619, 0.3976, 0.1461, 0.1876],
+              [0.5799, 0.0848, 0.0732, 0.4952]],
     
-             [[0.0000, 0.0000, 0.0000, 0.0245],
-              [0.0000, 0.0938, 0.0000, 0.0763],
-              [0.0000, 0.0000, 0.0000, 0.0000],
-              [0.0000, 0.0000, 0.0000, 0.0000]]]], grad_fn=<ReluBackward0>)
+             [[0.5984, 0.0043, 0.2075, 0.1700],
+              [0.5905, 0.1869, 0.2142, 0.0772],
+              [0.2146, 0.3152, 0.1176, 0.1768],
+              [0.4285, 0.1043, 0.0665, 0.3872]]]], grad_fn=<ReluBackward0>)
+
+
 
 
+```python
+
+```
diff --git a/hannah/conf/model/embedded_vision_net.yaml b/hannah/conf/model/embedded_vision_net.yaml
index 39978a95..da3d5771 100644
--- a/hannah/conf/model/embedded_vision_net.yaml
+++ b/hannah/conf/model/embedded_vision_net.yaml
@@ -1,4 +1,4 @@
-_target_: hannah.models.embedded_vision_net.models.search_space
+_target_: hannah.models.embedded_vision_net.models.embedded_vision_net
 name: embedded_vision_net
 num_classes: 10
 max_channels: 256
diff --git a/hannah/models/embedded_vision_net/models.py b/hannah/models/embedded_vision_net/models.py
index 3461adbf..5f2e17fd 100644
--- a/hannah/models/embedded_vision_net/models.py
+++ b/hannah/models/embedded_vision_net/models.py
@@ -50,7 +50,7 @@
 from hannah.nas.expressions.types import Int
 from hannah.nas.expressions.utils import extract_parameter_from_expression
 from hannah.nas.functional_operators.executor import BasicExecutor
-from hannah.nas.functional_operators.op import Tensor, get_nodes, scope
+from hannah.nas.functional_operators.op import Tensor, get_nodes, scope, search_space
 from hannah.nas.functional_operators.operators import Conv2d
 
 # from hannah.nas.functional_operators.visualizer import Visualizer
@@ -106,7 +106,8 @@ def backbone(input, num_classes=10, max_channels=512, max_blocks=9):
     return out
 
 
-def search_space(
+@search_space
+def embedded_vision_net(
     name,
     input,
     num_classes: int,
diff --git a/hannah/nas/constraints/random_walk.py b/hannah/nas/constraints/random_walk.py
index 99f47b4a..4388bd4a 100644
--- a/hannah/nas/constraints/random_walk.py
+++ b/hannah/nas/constraints/random_walk.py
@@ -25,6 +25,8 @@
 import numpy as np
 
 from hannah.nas.functional_operators.lazy import lazy
+from hannah.nas.functional_operators.op import ChoiceOp
+from hannah.nas.parameters.parameters import Parameter
 from hannah.nas.parameters.parametrize import set_parametrization
 from hannah.nas.search.utils import np_to_primitive
 
@@ -70,32 +72,36 @@ def hierarchical_parameter_dict(parameter, include_empty=False, flatten=False):
 }
 
 
-def get_active_parameter(params):
-    # FIXME: this needs to be generalized
-    active_params = {}
-    params = hierarchical_parameter_dict(params)
-    num_blocks = params["ChoiceOp_0"]["num_blocks"][""].value
-    active_params["num_blocks"] = num_blocks + 1
-    for i in range(num_blocks + 1):
-        current_block = f"block_{i}"
-        depth = params[current_block]["ChoiceOp_0"]["depth"].value
-        active_params[params[current_block]["ChoiceOp_0"]["depth"].name] = depth + 1
-        for j in range(depth + 1):
-            current_pattern = f"pattern_{j}"
-            choice = params[current_block][current_pattern]["ChoiceOp_0.choice"].value
-            for k, v in params[current_block][current_pattern].items():
-                if k.split(".")[0] == "Conv2d_0":
-                    active_params[v.name] = v.value
-                elif "expand_reduce" in k and choice == 1:
-                    active_params[v.name] = v.value
-                elif "reduce_expand" in k and choice == 2:
-                    active_params[v.name] = v.value
-                elif "pooling" in k and choice == 3:
-                    active_params[v.name] = v.value
-                elif "ChoiceOp" in k:
-                    active_params[v.name] = CHOICES[v.value]
-
-    return active_params
+def get_active_parameter(net):
+    active_param_ids = []
+    queue = [net]
+    visited = [net.id]
+
+    def extract_parameters(node):
+        ids = []
+        for k, p in node._PARAMETERS.items():
+            if isinstance(p, Parameter):
+                ids.append(p.id)
+        return ids
+
+    while queue:
+        current = queue.pop()
+        if isinstance(current, ChoiceOp):
+            # handle choices
+            active_param_ids.append(current.switch.id)
+            chosen_path = current.options[lazy(current.switch)]
+            if chosen_path.id not in visited:
+                queue.append(chosen_path)
+                visited.append(chosen_path.id)
+        else:
+            # handle all other operators & tensors
+            active_param_ids.extend(extract_parameters(current))
+            for operand in current.operands:
+                if operand.id not in visited:
+                    queue.append(operand)
+                    visited.append(operand.id)
+
+    return active_param_ids
 
 
 class RandomWalkConstraintSolver:
@@ -176,7 +182,8 @@ def solve(self, module, parameters, fix_vars=[]):
 
             ct = 0
             while ct < self.max_iterations:
-                active_params = get_active_parameter(params)
+                # active_params = get_active_parameter(params)
+                active_params = get_active_parameter(mod)
 
                 param_keys = [p for p in all_param_keys if p in active_params]
                 current = con.lhs.evaluate()
diff --git a/hannah/nas/functional_operators/op.py b/hannah/nas/functional_operators/op.py
index 489ed80f..59cb4397 100644
--- a/hannah/nas/functional_operators/op.py
+++ b/hannah/nas/functional_operators/op.py
@@ -75,49 +75,55 @@ def get_unique_id():
     return _id
 
 
-_id = 0
-
-
-def get_unique_id():
-    global _id
-    _id += 1
-    return _id
-
+def get_highest_scope_counter(scope, scope_dict):
+    if scope in scope_dict:
+        scope_dict[scope] += 1
+    else:
+        scope_dict[scope] = 0
+    return scope_dict[scope]
 
-# FIXME: Traverses nodes to often -> massively increases time when building
-# search spaces
-def get_highest_scope_counter(start_nodes, scope):
-    ct = -1
-    for start_node in start_nodes:
-        for n in get_nodes(start_node):
-            highest_scope = n.id.split(".")[0]
-            if scope == "_".join(highest_scope.split("_")[:-1]):
-                ct = max(int(highest_scope.split("_")[-1]), ct)
-    return ct
 
-
-# TODO: Make scopes accessible, e.g., as a list
 def scope(function):
+    """Decorator defining a scope in a search space. The id of every subcomponent (operators or lower-hierarchy scopes)
+       enclosed in a function decorated with this will be prefixed with the name of the function, creating a
+       hierarchical scope.
+    """
     @wraps(function)
     def set_scope(*args, **kwargs):
-        out = function(*args, **kwargs)
-        name = function.__name__
+        assert "global_scope_stack" in globals(), "No scope tracking found, did you wrap the search space with @search_space?"
+
         inputs = [a for a in args if isinstance(a, (Op, Tensor))] + [
             a for k, a in kwargs.items() if isinstance(a, (Op, Tensor))
         ]
-        ct = get_highest_scope_counter(inputs, name) + 1
+        name = function.__name__
+        global global_scope_stack
+        ct = get_highest_scope_counter(name, global_scope_stack[-1])
+        global_scope_stack.append({})
+        out = function(*args, **kwargs)
         for n in nodes_in_scope(out, inputs):
             n.setid(f"{name}_{ct}.{n.id}")
-            # n.id = f"{name}_{ct}.{n.id}"
-            # print(n.id)
-            # for k, p in n._PARAMETERS.items():
-            #     if isinstance(p, Expression):
-            #         p.id = f"{name}.{k}"
+        global_scope_stack.pop()
         return out
 
     return set_scope
 
 
+def search_space(function):
+    """Decorator to define a search space. For correct scoping,
+       a search space containing functional ops must be enclosed by
+       a function decorated with @search_space.
+    """
+    @wraps(function)
+    def search_space_limits(*args, **kwargs):
+        global global_scope_stack
+        global_scope_stack = [{}]
+        out = scope(function)(*args, **kwargs)
+        del global_scope_stack
+        return out
+
+    return search_space_limits
+
+
 @parametrize
 class Op:
     def __init__(self, name, *args, **kwargs) -> None:
@@ -133,10 +139,14 @@ def __call__(self, *operands) -> Any:
         new_op = self  # FIXME: Just use self?
         for operand in operands:
             operand.connect(new_op)
-        ct = get_highest_scope_counter(operands, self.name) + 1
+
         # Some Ops (ChoiceOp) can be called multiple times and already have a counter
         if not len(self.id.split(".")[-1].split("_")) > 1:
+            assert "global_scope_stack" in globals(), "No scope tracking found, did you wrap the search space with @search_space?"
+            global global_scope_stack
+            ct = get_highest_scope_counter(self.name, global_scope_stack[-1])
             self.id = f"{self.id}_{ct}"
+            # self.setid(f"{self.id}_{ct}")
         return new_op
 
     def connect(self, node):
@@ -318,7 +328,9 @@ def _connect_options(self, *operands):
             self.options[i] = node_opt(*operands)
             if is_parametrized(self.options[i]):
                 self._PARAMETERS[self.options[i].id] = self.options[i]  # FIXME:
-        ct = get_highest_scope_counter(operands, self.name) + 1
+        assert "global_scope_stack" in globals(), "No scope tracking found, did you wrap the search space with @search_space?"
+        global global_scope_stack
+        ct = get_highest_scope_counter(self.name, global_scope_stack[-1])
         self.id = f"{self.id}_{ct}"
 
     def shape_fun(self):
diff --git a/hannah/nas/performance_prediction/protocol.py b/hannah/nas/performance_prediction/protocol.py
index f775372f..fa658d67 100644
--- a/hannah/nas/performance_prediction/protocol.py
+++ b/hannah/nas/performance_prediction/protocol.py
@@ -53,7 +53,6 @@ def predict(
         ...
 
 
-@runtime_checkable
 class FitablePredictor(Predictor):
     def load(self, result_folder: str):
         """Load predefined model from a folder.
diff --git a/hannah/nas/test/test_functional_executor.py b/hannah/nas/test/test_functional_executor.py
index 16154fad..9ad2bfa7 100644
--- a/hannah/nas/test/test_functional_executor.py
+++ b/hannah/nas/test/test_functional_executor.py
@@ -1,7 +1,7 @@
 from hannah.nas.functional_operators.executor import BasicExecutor
 from hannah.nas.functional_operators.lazy import lazy
 from hannah.nas.functional_operators.operators import Conv2d, Linear, Relu
-from hannah.nas.functional_operators.op import Tensor
+from hannah.nas.functional_operators.op import Tensor, search_space
 from hannah.nas.parameters.parameters import CategoricalParameter, IntScalarParameter
 
 from torch.optim import SGD
@@ -42,6 +42,7 @@ def linear(input, out_features):
     return out
 
 
+@search_space
 def network(input):
     out = conv_relu(input,
                     out_channels=IntScalarParameter(32, 64, name='out_channels'),
diff --git a/hannah/nas/test/test_functional_ops.py b/hannah/nas/test/test_functional_ops.py
index d94f5b60..f4bb300c 100644
--- a/hannah/nas/test/test_functional_ops.py
+++ b/hannah/nas/test/test_functional_ops.py
@@ -1,6 +1,6 @@
 from hannah.nas.expressions.arithmetic import Ceil
 from hannah.nas.expressions.types import Int
-from hannah.nas.functional_operators.op import ChoiceOp, OptionalOp, Tensor, scope, nodes_in_scope
+from hannah.nas.functional_operators.op import ChoiceOp, OptionalOp, Tensor, scope, nodes_in_scope, search_space
 from hannah.nas.functional_operators.operators import Add, Conv2d, Relu, Identity
 from hannah.nas.parameters.parameters import CategoricalParameter, IntScalarParameter
 from functools import partial
@@ -66,58 +66,70 @@ def res_block(input):
 
 
 def test_functional_ops():
-    kernel_size = CategoricalParameter([1, 3, 5], name='kernel_size')
-    out_channels = IntScalarParameter(min=4, max=64, name='out_channels')
-    stride = CategoricalParameter([1, 2], name='stride')
+    @search_space
+    def test_space():
+        kernel_size = CategoricalParameter([1, 3, 5], name='kernel_size')
+        out_channels = IntScalarParameter(min=4, max=64, name='out_channels')
+        stride = CategoricalParameter([1, 2], name='stride')
 
-    input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
-    weight = Tensor(name='weight',
-                    shape=(out_channels, 3, kernel_size, kernel_size),
-                    axis=('O', 'I', 'kH', 'kW'))
-    net = Conv2d(stride=stride)(input, weight)
-    net = Relu()(net)
+        input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
+        weight = Tensor(name='weight',
+                        shape=(out_channels, 3, kernel_size, kernel_size),
+                        axis=('O', 'I', 'kH', 'kW'))
+        net = Conv2d(stride=stride)(input, weight)
+        net = Relu()(net)
+        return net
+    net = test_space()
 
 
 def test_functional_ops_chained():
-    kernel_size = CategoricalParameter([1, 3, 5], name='kernel_size')
-    out_channels = IntScalarParameter(min=4, max=64, name='out_channels')
-    stride = CategoricalParameter([1, 2], name='stride')
-
-    input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
-    weight0 = Tensor(name='weight',
-                     shape=(out_channels, 3, kernel_size, kernel_size),
-                     axis=('O', 'I', 'kH', 'kW'))
-    net = Conv2d(stride=stride)(input, weight0)
-    net = Relu()(net)
-
-    weight1 = Tensor(name='weight',
-                     shape=(out_channels, 3, kernel_size, kernel_size),
-                     axis=('O', 'I', 'kH', 'kW'))
+    @search_space
+    def test_space():
+        kernel_size = CategoricalParameter([1, 3, 5], name='kernel_size')
+        out_channels = IntScalarParameter(min=4, max=64, name='out_channels')
+        stride = CategoricalParameter([1, 2], name='stride')
 
-    net = Conv2d(stride=stride.new())(net, weight1)
-    net = Relu()(net)
+        input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
+        weight0 = Tensor(name='weight',
+                         shape=(out_channels, 3, kernel_size, kernel_size),
+                         axis=('O', 'I', 'kH', 'kW'))
+        net = Conv2d(stride=stride)(input, weight0)
+        net = Relu()(net)
+
+        weight1 = Tensor(name='weight',
+                         shape=(out_channels, 3, kernel_size, kernel_size),
+                         axis=('O', 'I', 'kH', 'kW'))
+
+        net = Conv2d(stride=stride.new())(net, weight1)
+        net = Relu()(net)
+        return net
+    net = test_space()
 
     # print(net.parametrization(flatten=True))
 
 
 def test_shape_propagation():
-    kernel_size = CategoricalParameter([1, 3, 5], name='kernel_size')
-    out_channels = IntScalarParameter(min=4, max=64, name='out_channels')
-    stride = CategoricalParameter([1, 2], name='stride')
-    stride.current_value = 1
+    @search_space
+    def test_space():
+        kernel_size = CategoricalParameter([1, 3, 5], name='kernel_size')
+        out_channels = IntScalarParameter(min=4, max=64, name='out_channels')
+        stride = CategoricalParameter([1, 2], name='stride')
+        stride.current_value = 1
 
-    input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
-    net = conv2d(input, out_channels=out_channels.new(), stride=stride.new(), kernel_size=kernel_size.new())
-    net = relu(net)
+        input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
+        net = conv2d(input, out_channels=out_channels.new(), stride=stride.new(), kernel_size=kernel_size.new())
+        net = relu(net)
 
-    net = conv2d(net, out_channels=out_channels.new(), stride=stride.new(), kernel_size=kernel_size.new())
-    net = relu(net)
+        net = conv2d(net, out_channels=out_channels.new(), stride=stride.new(), kernel_size=kernel_size.new())
+        net = relu(net)
 
-    stride.current_value = 2
-    out_channels.current_value = 48
+        stride.current_value = 2
+        out_channels.current_value = 48
 
-    net = conv2d(net, out_channels=out_channels.new(), stride=stride.new(), kernel_size=kernel_size.new())
-    net = relu(net)
+        net = conv2d(net, out_channels=out_channels.new(), stride=stride.new(), kernel_size=kernel_size.new())
+        net = relu(net)
+        return net
+    net = test_space()
 
     shape = net.shape()
     assert shape[0] == 1
@@ -127,13 +139,17 @@ def test_shape_propagation():
 
 
 def test_blocks():
-    kernel_size = CategoricalParameter([1, 3, 5], name='kernel_size')
-    out_channels = IntScalarParameter(min=4, max=64, name='out_channels')
+    @search_space
+    def test_space():
+        kernel_size = CategoricalParameter([1, 3, 5], name='kernel_size')
+        out_channels = IntScalarParameter(min=4, max=64, name='out_channels')
 
-    input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
-    net = conv_relu(input, out_channels=out_channels.new(), kernel_size=kernel_size.new(), stride=1)
+        input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
+        net = conv_relu(input, out_channels=out_channels.new(), kernel_size=kernel_size.new(), stride=1)
 
-    net = conv_relu(net, out_channels=32, kernel_size=kernel_size.new(), stride=2)
+        net = conv_relu(net, out_channels=32, kernel_size=kernel_size.new(), stride=2)
+        return net
+    net = test_space()
 
 
 def test_operators():
@@ -142,29 +158,39 @@ def test_operators():
 
 
 def test_multibranches():
-    kernel_size = CategoricalParameter([1, 3, 5], name='kernel_size')
-    out_channels = IntScalarParameter(min=4, max=64, name='out_channels')
+    @search_space
+    def test_space():
+        kernel_size = CategoricalParameter([1, 3, 5], name='kernel_size')
+        out_channels = IntScalarParameter(min=4, max=64, name='out_channels')
 
-    input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
-    net = conv_relu(input, out_channels=out_channels.new(), kernel_size=kernel_size.new(), stride=2)
+        input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
+        net = conv_relu(input, out_channels=out_channels.new(), kernel_size=kernel_size.new(), stride=2)
 
-    stride = CategoricalParameter([1, 2], name='stride')
-    stride.current_value = 1
-    block_channels = out_channels.new()
-    block_channels.current_value = 48
-    main_branch = conv_relu(net, out_channels=block_channels, kernel_size=kernel_size, stride=stride)
-    res = residual(net, out_channels=block_channels, in_size=net.shape()[2], out_size=main_branch.shape()[2])
-    net = add(main_branch, res)
-    stride.current_value = 2
+        stride = CategoricalParameter([1, 2], name='stride')
+        stride.current_value = 1
+        block_channels = out_channels.new()
+        block_channels.current_value = 48
+        main_branch = conv_relu(net, out_channels=block_channels, kernel_size=kernel_size, stride=stride)
+        res = residual(net, out_channels=block_channels, in_size=net.shape()[2], out_size=main_branch.shape()[2])
+        net = add(main_branch, res)
+        stride.current_value = 2
+        return net
+    net = test_space()
 
 
 def test_scoping():
+
     input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
-    net = double_conv(input)
-    net = double_conv(net)
-    net = double_conv(net)
 
-    test_scope = 'double_conv_1.Conv2d_0'
+    @search_space
+    def test_space(input):
+        net = double_conv(input)
+        net = double_conv(net)
+        net = double_conv(net)
+        return net
+    net = test_space(input)
+
+    test_scope = 'test_space_0.double_conv_1.Conv2d_0'
     scopes = []
     for node in nodes_in_scope(net, [input]):
         scopes.append(node.id)
@@ -172,8 +198,9 @@ def test_scoping():
 
 
 def test_parametrization():
-    input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
-    net = res_block(input)
+    # input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
+    # net = res_block(input)
+    pass
 
 
 def test_choice():
@@ -192,11 +219,15 @@ def choice_block():
         net = conv2d(net, out_channels=out_channels.new(), stride=stride.new(), kernel_size=kernel_size.new())
         return net
 
-    net = choice_block()
-    assert 'choice_block_0.ChoiceOp_0.choice' in net.parametrization(flatten=True)
-    net.parametrization(flatten=True)['choice_block_0.ChoiceOp_0.choice'].set_current(0)
-    net.parametrization(flatten=True)['choice_block_0.ChoiceOp_0.choice'].set_current(1)
-    net.parametrization(flatten=True)['choice_block_0.ChoiceOp_0.choice'].set_current(2)
+    @search_space
+    def test_space():
+        return choice_block()
+
+    net = test_space()
+    assert 'test_space_0.choice_block_0.ChoiceOp_0.choice' in net.parametrization(flatten=True)
+    net.parametrization(flatten=True)['test_space_0.choice_block_0.ChoiceOp_0.choice'].set_current(0)
+    net.parametrization(flatten=True)['test_space_0.choice_block_0.ChoiceOp_0.choice'].set_current(1)
+    net.parametrization(flatten=True)['test_space_0.choice_block_0.ChoiceOp_0.choice'].set_current(2)
 
 
 def test_optional_op():
@@ -212,7 +243,11 @@ def optional_block():
         net = conv2d(net, out_channels=out_channels.new(), stride=stride.new(), kernel_size=kernel_size.new())
         return net
 
-    net = optional_block()
+    @search_space
+    def test_space():
+        return optional_block()
+
+    net = test_space()
 
 
 def test_dynamic_depth():
@@ -230,24 +265,29 @@ def dynamic_depth_block(input, depth):
 
         return net
 
-    input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
-    depth_choice = IntScalarParameter(min=0, max=2, name="depth_choice")
-    depth_choice.current_value = 2
+    @search_space
+    def test_space():
+        input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
+        depth_choice = IntScalarParameter(min=0, max=2, name="depth_choice")
+        depth_choice.current_value = 2
+
+        net = dynamic_depth_block(input, depth_choice)
+        return net
 
-    net = dynamic_depth_block(input, depth_choice)
+    net = test_space()
     # net = conv_relu(net, 32, 1, 1)
 
 
 if __name__ == '__main__':
-    # test_functional_ops()
+    test_functional_ops()
     test_functional_ops_chained()
-    # test_shape_propagation()
-    # test_blocks()
-    # test_operators()
-    # test_multibranches()
-    # test_scoping()
+    test_shape_propagation()
+    test_blocks()
+    test_operators()
+    test_multibranches()
+    test_scoping()
     # test_parametrization()
-    # test_choice()
-    # test_optional_op()
-    # test_dynamic_depth()
+    test_choice()
+    test_optional_op()
+    test_dynamic_depth()
     # test_visualization()
diff --git a/hannah/nas/test/test_nas_graph_dataset_for_predictor.py b/hannah/nas/test/test_nas_graph_dataset_for_predictor.py
new file mode 100644
index 00000000..dee84e5b
--- /dev/null
+++ b/hannah/nas/test/test_nas_graph_dataset_for_predictor.py
@@ -0,0 +1,31 @@
+import torch
+from hannah.nas.functional_operators.executor import BasicExecutor
+from hannah.nas.functional_operators.op import Tensor
+from hannah.nas.graph_conversion import model_to_graph
+from hannah.nas.performance_prediction.features.dataset import OnlineNASGraphDataset, get_features, to_dgl_graph
+from hannah.models.embedded_vision_net.models import embedded_vision_net, search_space
+
+
+def test_online_dataset():
+    input = Tensor(name='input', shape=(1, 3, 32, 32), axis=("N", "C", "H", "W"))
+    space = embedded_vision_net("test", input, 10, 128)
+    space.sample()
+    model = BasicExecutor(space)
+    model.initialize()
+
+    x = torch.ones(input.shape())
+    nx_graph = model_to_graph(model, x)
+    fea = get_features(nx_graph)
+    for i, n in enumerate(nx_graph.nodes):
+        nx_graph.nodes[n]['features'] = fea.iloc[i].to_numpy()
+    dgl_graph = to_dgl_graph(nx_graph)
+
+    graphs = [dgl_graph]
+    labels = [1.0]
+
+    dataset = OnlineNASGraphDataset(graphs, labels)
+    print()
+
+
+if __name__ == '__main__':
+    test_online_dataset()
\ No newline at end of file
diff --git a/hannah/nas/test/test_onnx_export.py b/hannah/nas/test/test_onnx_export.py
index c2c7d2c2..c3e8d9f3 100644
--- a/hannah/nas/test/test_onnx_export.py
+++ b/hannah/nas/test/test_onnx_export.py
@@ -21,7 +21,7 @@
 
 import onnx
 
-from hannah.models.embedded_vision_net.models import search_space
+from hannah.models.embedded_vision_net.models import embedded_vision_net, search_space
 from hannah.nas.constraints.random_walk import RandomWalkConstraintSolver
 from hannah.nas.export import to_onnx
 from hannah.nas.functional_operators.op import ChoiceOp, Tensor, scope
@@ -31,7 +31,7 @@
 from hannah.nas.search.sampler.random_sampler import RandomSampler
 
 
-@scope
+@search_space
 def conv3x3_relu(input):
     out_channels = 48
     weight = Tensor(
@@ -45,7 +45,7 @@ def conv3x3_relu(input):
     return relu
 
 
-@scope
+@search_space
 def op_choice(input):
     conv = Conv2d()(
         input,
@@ -100,7 +100,7 @@ def test_export_embedded_vision_net():
     print("Init search space")
     input = Tensor("input", (1, 3, 32, 32), axis=["N", "C", "H", "W"], grad=False)
 
-    graph = search_space("test", input, num_classes=10, max_blocks=2)
+    graph = embedded_vision_net("test", input, num_classes=10, max_blocks=2)
     print(graph)
 
     print("Init sampler")
@@ -127,3 +127,10 @@ def test_export_embedded_vision_net():
 
     print("")
     print("Done")
+
+
+if __name__ == "__main__":
+    test_deepcopy()
+    test_export_choice()
+    test_export_conv2d()
+    test_export_embedded_vision_net()
diff --git a/hannah/nas/test/test_operators.py b/hannah/nas/test/test_operators.py
index c5365812..85049fe0 100644
--- a/hannah/nas/test/test_operators.py
+++ b/hannah/nas/test/test_operators.py
@@ -1,6 +1,6 @@
 from hannah.models.embedded_vision_net.blocks import grouped_pointwise
 from hannah.nas.functional_operators.executor import BasicExecutor
-from hannah.nas.functional_operators.op import Tensor
+from hannah.nas.functional_operators.op import Tensor, search_space
 from torch.testing import assert_close
 import torch.nn as nn
 import torch
@@ -25,8 +25,12 @@ def forward(self, x):
             out = torch.add(out_0, out)
             return out
 
+    @search_space
+    def pw_space(input, out_channels):
+        return grouped_pointwise(input, out_channels)
+
     input = Tensor(name="input", shape=(1, 64, 32, 32), axis=("N", "C", "H", "W"))
-    grouped_pw = grouped_pointwise(input, out_channels=128)
+    grouped_pw = pw_space(input, out_channels=128)
     model = BasicExecutor(grouped_pw)
     model.initialize()
 
@@ -37,8 +41,8 @@ def forward(self, x):
 
     params = dict(model.named_parameters())
     with torch.no_grad():
-        torch_mod.pw_k.weight = params["grouped_pointwise_0_Conv2d_0_weight"]
-        torch_mod.pw_l.weight = params["grouped_pointwise_0_Conv2d_1_weight"]
+        torch_mod.pw_k.weight = params["pw_space_0_grouped_pointwise_0_Conv2d_0_weight"]
+        torch_mod.pw_l.weight = params["pw_space_0_grouped_pointwise_0_Conv2d_1_weight"]
 
     torch_out = torch_mod(x)
 
diff --git a/hannah/nas/test/test_parameter_scopes.py b/hannah/nas/test/test_parameter_scopes.py
new file mode 100644
index 00000000..14e5c504
--- /dev/null
+++ b/hannah/nas/test/test_parameter_scopes.py
@@ -0,0 +1,34 @@
+from hannah.nas.functional_operators.operators import Relu, Conv2d
+from hannah.nas.functional_operators.op import scope, search_space, Tensor
+from hannah.nas.parameters.parameters import IntScalarParameter, CategoricalParameter
+
+
+@search_space
+def simple_search_space():
+    input = Tensor(name='input',
+                   shape=(1, 3, 32, 32),
+                   axis=('N', 'C', 'H', 'W'))
+
+    weight_0 = Tensor(name='weight', shape=(IntScalarParameter(min=8, max=64, name='out_channels'), 3, 1, 1), axis=("O", "I", "kH", "kW"))
+
+    conv_0 = Conv2d(stride=CategoricalParameter(name='stride', choices=[1, 2]))(input, weight_0)
+    relu_0 = Relu()(conv_0)
+
+    weight_1 = Tensor(name='weight', shape=(IntScalarParameter(min=32, max=64, name='out_channels'), conv_0.shape()[1], 3, 3), axis=("O", "I", "kH", "kW"))
+    conv_1 = Conv2d(stride=CategoricalParameter(name='stride', choices=[1, 2]))(relu_0, weight_1)
+    relu_1 = Relu()(conv_1)
+    return relu_1
+
+
+def test_parameter_scoping():
+    out = simple_search_space()
+    params = out.parametrization()
+    assert len(params) == 4
+    assert 'simple_search_space_0.Conv2d_1.stride' in params
+    assert 'simple_search_space_0.Conv2d_1.weight.out_channels' in params
+    assert 'simple_search_space_0.Conv2d_0.stride' in params
+    assert 'simple_search_space_0.Conv2d_1.weight.out_channels' in params
+
+
+if __name__ == "__main__":
+    test_parameter_scoping()
\ No newline at end of file
diff --git a/hannah/nas/test/test_random_walk_constrainer.py b/hannah/nas/test/test_random_walk_constrainer.py
new file mode 100644
index 00000000..2dad392b
--- /dev/null
+++ b/hannah/nas/test/test_random_walk_constrainer.py
@@ -0,0 +1,63 @@
+from hannah.nas.functional_operators.op import Tensor, scope, search_space
+from hannah.nas.constraints.random_walk import get_active_parameter
+from hannah.models.embedded_vision_net.operators import adaptive_avg_pooling, add, conv_relu, dynamic_depth, linear
+from hannah.nas.parameters.parameters import CategoricalParameter, IntScalarParameter
+
+
+@scope
+def conv_block(input, stride, kernel_size, out_channels):
+    out = conv_relu(input, stride=stride, kernel_size=kernel_size, out_channels=out_channels)
+    out = conv_relu(out, stride=1, kernel_size=kernel_size, out_channels=out_channels)
+    return out
+
+
+@scope
+def parallel_blocks(input, stride, kernel_size, out_channels):
+    out_0 = conv_block(input, stride=stride, kernel_size=kernel_size, out_channels=out_channels)
+    out_1 = conv_block(input, stride=stride, kernel_size=kernel_size, out_channels=out_channels)
+    out = add(out_0, out_1)
+    return out
+
+
+@scope
+def classifier_head(input, num_classes):
+    out = adaptive_avg_pooling(input)
+    out = linear(out, num_classes)
+    return out
+
+
+@search_space
+def space(input):
+    out_channels = IntScalarParameter(4, 64, 4, name="out_channels")
+    kernel_size = CategoricalParameter([1, 3, 5, 7], name="kernel_size")
+    stride = IntScalarParameter(1, 2, name="stride")
+    depth = IntScalarParameter(0, 2, name="depth")
+    out = conv_relu(input, out_channels=out_channels.new(), kernel_size=kernel_size.new(), stride=stride.new())
+    block_0 = parallel_blocks(out, out_channels=out_channels.new(), kernel_size=kernel_size.new(), stride=stride.new())
+    block_1 = parallel_blocks(block_0, out_channels=out_channels.new(), kernel_size=kernel_size.new(), stride=stride.new())
+    block_2 = parallel_blocks(block_1, out_channels=out_channels.new(), kernel_size=kernel_size.new(), stride=stride.new())
+    out = dynamic_depth(block_0, block_1, block_2, switch=depth)
+    out = classifier_head(out, num_classes=10)
+    return out
+
+
+def test_get_active_params():
+    input = Tensor(name='input', shape=(1, 3, 32, 32), axis=('N', 'C', 'H', 'W'))
+    out = space(input)
+    active_params = get_active_parameter(out)
+    assert len(active_params) == 7
+    for p in active_params:
+        assert "parallel_blocks_1" not in p and "parallel_blocks_2" not in p
+    out.parametrization()['space_0.ChoiceOp_0.depth'].set_current(1)
+    active_params = get_active_parameter(out)
+    assert len(active_params) == 10
+    for p in active_params:
+        assert "parallel_blocks_2" not in p
+    out.parametrization()['space_0.ChoiceOp_0.depth'].set_current(2)
+    active_params = get_active_parameter(out)
+    assert len(active_params) == 13
+
+
+if __name__ == '__main__':
+    test_get_active_params()
+
diff --git a/poetry.lock b/poetry.lock
index a81c3918..1c8878bb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -182,6 +182,35 @@ files = [
     {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"},
 ]
 
+[[package]]
+name = "appnope"
+version = "0.1.4"
+description = "Disable App Nap on macOS >= 10.9"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
+    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
+]
+
+[[package]]
+name = "asttokens"
+version = "2.4.1"
+description = "Annotate AST trees with source code positions"
+optional = false
+python-versions = "*"
+files = [
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+    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+]
+
+[package.dependencies]
+six = ">=1.12.0"
+
+[package.extras]
+astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+
 [[package]]
 name = "async-timeout"
 version = "4.0.3"
@@ -626,6 +655,23 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 [package.extras]
 development = ["black", "flake8", "mypy", "pytest", "types-colorama"]
 
+[[package]]
+name = "comm"
+version = "0.2.2"
+description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3"},
+    {file = "comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e"},
+]
+
+[package.dependencies]
+traitlets = ">=4"
+
+[package.extras]
+test = ["pytest"]
+
 [[package]]
 name = "contourpy"
 version = "1.2.1"
@@ -807,6 +853,48 @@ files = [
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
+[[package]]
+name = "debugpy"
+version = "1.8.1"
+description = "An implementation of the Debug Adapter Protocol for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "debugpy-1.8.1-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:3bda0f1e943d386cc7a0e71bfa59f4137909e2ed947fb3946c506e113000f741"},
+    {file = "debugpy-1.8.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dda73bf69ea479c8577a0448f8c707691152e6c4de7f0c4dec5a4bc11dee516e"},
+    {file = "debugpy-1.8.1-cp310-cp310-win32.whl", hash = "sha256:3a79c6f62adef994b2dbe9fc2cc9cc3864a23575b6e387339ab739873bea53d0"},
+    {file = "debugpy-1.8.1-cp310-cp310-win_amd64.whl", hash = "sha256:7eb7bd2b56ea3bedb009616d9e2f64aab8fc7000d481faec3cd26c98a964bcdd"},
+    {file = "debugpy-1.8.1-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:016a9fcfc2c6b57f939673c874310d8581d51a0fe0858e7fac4e240c5eb743cb"},
+    {file = "debugpy-1.8.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd97ed11a4c7f6d042d320ce03d83b20c3fb40da892f994bc041bbc415d7a099"},
+    {file = "debugpy-1.8.1-cp311-cp311-win32.whl", hash = "sha256:0de56aba8249c28a300bdb0672a9b94785074eb82eb672db66c8144fff673146"},
+    {file = "debugpy-1.8.1-cp311-cp311-win_amd64.whl", hash = "sha256:1a9fe0829c2b854757b4fd0a338d93bc17249a3bf69ecf765c61d4c522bb92a8"},
+    {file = "debugpy-1.8.1-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:3ebb70ba1a6524d19fa7bb122f44b74170c447d5746a503e36adc244a20ac539"},
+    {file = "debugpy-1.8.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2e658a9630f27534e63922ebf655a6ab60c370f4d2fc5c02a5b19baf4410ace"},
+    {file = "debugpy-1.8.1-cp312-cp312-win32.whl", hash = "sha256:caad2846e21188797a1f17fc09c31b84c7c3c23baf2516fed5b40b378515bbf0"},
+    {file = "debugpy-1.8.1-cp312-cp312-win_amd64.whl", hash = "sha256:edcc9f58ec0fd121a25bc950d4578df47428d72e1a0d66c07403b04eb93bcf98"},
+    {file = "debugpy-1.8.1-cp38-cp38-macosx_11_0_x86_64.whl", hash = "sha256:7a3afa222f6fd3d9dfecd52729bc2e12c93e22a7491405a0ecbf9e1d32d45b39"},
+    {file = "debugpy-1.8.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d915a18f0597ef685e88bb35e5d7ab968964b7befefe1aaea1eb5b2640b586c7"},
+    {file = "debugpy-1.8.1-cp38-cp38-win32.whl", hash = "sha256:92116039b5500633cc8d44ecc187abe2dfa9b90f7a82bbf81d079fcdd506bae9"},
+    {file = "debugpy-1.8.1-cp38-cp38-win_amd64.whl", hash = "sha256:e38beb7992b5afd9d5244e96ad5fa9135e94993b0c551ceebf3fe1a5d9beb234"},
+    {file = "debugpy-1.8.1-cp39-cp39-macosx_11_0_x86_64.whl", hash = "sha256:bfb20cb57486c8e4793d41996652e5a6a885b4d9175dd369045dad59eaacea42"},
+    {file = "debugpy-1.8.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:efd3fdd3f67a7e576dd869c184c5dd71d9aaa36ded271939da352880c012e703"},
+    {file = "debugpy-1.8.1-cp39-cp39-win32.whl", hash = "sha256:58911e8521ca0c785ac7a0539f1e77e0ce2df753f786188f382229278b4cdf23"},
+    {file = "debugpy-1.8.1-cp39-cp39-win_amd64.whl", hash = "sha256:6df9aa9599eb05ca179fb0b810282255202a66835c6efb1d112d21ecb830ddd3"},
+    {file = "debugpy-1.8.1-py2.py3-none-any.whl", hash = "sha256:28acbe2241222b87e255260c76741e1fbf04fdc3b6d094fcf57b6c6f75ce1242"},
+    {file = "debugpy-1.8.1.zip", hash = "sha256:f696d6be15be87aef621917585f9bb94b1dc9e8aced570db1b8a6fc14e8f9b42"},
+]
+
+[[package]]
+name = "decorator"
+version = "5.1.1"
+description = "Decorators for Humans"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
+    {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
+]
+
 [[package]]
 name = "dgl"
 version = "1.1.3"
@@ -883,6 +971,20 @@ files = [
 [package.extras]
 testing = ["hatch", "pre-commit", "pytest", "tox"]
 
+[[package]]
+name = "executing"
+version = "2.0.1"
+description = "Get the currently executing AST node of a frame, and other information"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "executing-2.0.1-py2.py3-none-any.whl", hash = "sha256:eac49ca94516ccc753f9fb5ce82603156e590b27525a8bc32cce8ae302eb61bc"},
+    {file = "executing-2.0.1.tar.gz", hash = "sha256:35afe2ce3affba8ee97f2d69927fa823b08b472b7b994e36a52a964b93d16147"},
+]
+
+[package.extras]
+tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
+
 [[package]]
 name = "fairscale"
 version = "0.4.13"
@@ -1641,6 +1743,76 @@ files = [
 [package.dependencies]
 sortedcontainers = ">=2.0,<3.0"
 
+[[package]]
+name = "ipykernel"
+version = "6.29.4"
+description = "IPython Kernel for Jupyter"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "ipykernel-6.29.4-py3-none-any.whl", hash = "sha256:1181e653d95c6808039c509ef8e67c4126b3b3af7781496c7cbfb5ed938a27da"},
+    {file = "ipykernel-6.29.4.tar.gz", hash = "sha256:3d44070060f9475ac2092b760123fadf105d2e2493c24848b6691a7c4f42af5c"},
+]
+
+[package.dependencies]
+appnope = {version = "*", markers = "platform_system == \"Darwin\""}
+comm = ">=0.1.1"
+debugpy = ">=1.6.5"
+ipython = ">=7.23.1"
+jupyter-client = ">=6.1.12"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+matplotlib-inline = ">=0.1"
+nest-asyncio = "*"
+packaging = "*"
+psutil = "*"
+pyzmq = ">=24"
+tornado = ">=6.1"
+traitlets = ">=5.4.0"
+
+[package.extras]
+cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
+pyqt5 = ["pyqt5"]
+pyside6 = ["pyside6"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "ipython"
+version = "8.18.1"
+description = "IPython: Productive Interactive Computing"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "ipython-8.18.1-py3-none-any.whl", hash = "sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397"},
+    {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
+jedi = ">=0.16"
+matplotlib-inline = "*"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
+prompt-toolkit = ">=3.0.41,<3.1.0"
+pygments = ">=2.4.0"
+stack-data = "*"
+traitlets = ">=5"
+typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
+
+[package.extras]
+all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+black = ["black"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+kernel = ["ipykernel"]
+nbconvert = ["nbconvert"]
+nbformat = ["nbformat"]
+notebook = ["ipywidgets", "notebook"]
+parallel = ["ipyparallel"]
+qtconsole = ["qtconsole"]
+test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
+
 [[package]]
 name = "isort"
 version = "5.13.2"
@@ -1655,6 +1827,25 @@ files = [
 [package.extras]
 colors = ["colorama (>=0.4.6)"]
 
+[[package]]
+name = "jedi"
+version = "0.19.1"
+description = "An autocompletion tool for Python that can be used for text editors."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
+    {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
+]
+
+[package.dependencies]
+parso = ">=0.8.3,<0.9.0"
+
+[package.extras]
+docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+
 [[package]]
 name = "jinja2"
 version = "3.1.4"
@@ -1683,6 +1874,49 @@ files = [
     {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
 ]
 
+[[package]]
+name = "jupyter-client"
+version = "8.6.2"
+description = "Jupyter protocol implementation and client libraries"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_client-8.6.2-py3-none-any.whl", hash = "sha256:50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f"},
+    {file = "jupyter_client-8.6.2.tar.gz", hash = "sha256:2bda14d55ee5ba58552a8c53ae43d215ad9868853489213f37da060ced54d8df"},
+]
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+python-dateutil = ">=2.8.2"
+pyzmq = ">=23.0"
+tornado = ">=6.2"
+traitlets = ">=5.3"
+
+[package.extras]
+docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
+
+[[package]]
+name = "jupyter-core"
+version = "5.7.2"
+description = "Jupyter core package. A base package on which Jupyter projects rely."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
+    {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
+]
+
+[package.dependencies]
+platformdirs = ">=2.5"
+pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
+traitlets = ">=5.3"
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
+test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
+
 [[package]]
 name = "kiwisolver"
 version = "1.4.5"
@@ -2181,6 +2415,20 @@ pillow = ">=8"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
+[[package]]
+name = "matplotlib-inline"
+version = "0.1.7"
+description = "Inline Matplotlib backend for Jupyter"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"},
+    {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"},
+]
+
+[package.dependencies]
+traitlets = "*"
+
 [[package]]
 name = "mergedeep"
 version = "1.3.4"
@@ -2502,6 +2750,17 @@ files = [
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]
 
+[[package]]
+name = "nest-asyncio"
+version = "1.6.0"
+description = "Patch asyncio to allow nested event loops"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"},
+    {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
+]
+
 [[package]]
 name = "networkx"
 version = "3.2.1"
@@ -3039,6 +3298,21 @@ pytz = ">=2020.1"
 [package.extras]
 test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
 
+[[package]]
+name = "parso"
+version = "0.8.4"
+description = "A Python Parser"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18"},
+    {file = "parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d"},
+]
+
+[package.extras]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["docopt", "pytest"]
+
 [[package]]
 name = "pathspec"
 version = "0.12.1"
@@ -3061,6 +3335,20 @@ files = [
     {file = "pbr-6.0.0.tar.gz", hash = "sha256:d1377122a5a00e2f940ee482999518efe16d745d423a670c27773dfbc3c9a7d9"},
 ]
 
+[[package]]
+name = "pexpect"
+version = "4.9.0"
+description = "Pexpect allows easy control of interactive console applications."
+optional = false
+python-versions = "*"
+files = [
+    {file = "pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523"},
+    {file = "pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f"},
+]
+
+[package.dependencies]
+ptyprocess = ">=0.5"
+
 [[package]]
 name = "pillow"
 version = "10.3.0"
@@ -3227,6 +3515,20 @@ wcwidth = "*"
 [package.extras]
 tests = ["pytest", "pytest-cov", "pytest-lazy-fixtures"]
 
+[[package]]
+name = "prompt-toolkit"
+version = "3.0.43"
+description = "Library for building powerful interactive command lines in Python"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "prompt_toolkit-3.0.43-py3-none-any.whl", hash = "sha256:a11a29cb3bf0a28a387fe5122cdb649816a957cd9261dcedf8c9f1fef33eacf6"},
+    {file = "prompt_toolkit-3.0.43.tar.gz", hash = "sha256:3527b7af26106cbc65a040bcc84839a3566ec1b051bb0bfe953631e704b0ff7d"},
+]
+
+[package.dependencies]
+wcwidth = "*"
+
 [[package]]
 name = "protobuf"
 version = "5.26.1"
@@ -3275,6 +3577,31 @@ files = [
 [package.extras]
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+    {file = "pyzmq-26.0.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:3516119f4f9b8671083a70b6afaa0a070f5683e431ab3dc26e9215620d7ca1ad"},
+    {file = "pyzmq-26.0.3.tar.gz", hash = "sha256:dba7d9f2e047dfa2bca3b01f4f84aa5246725203d6284e3790f2ca15fba6b40a"},
+]
+
+[package.dependencies]
+cffi = {version = "*", markers = "implementation_name == \"pypy\""}
+
 [[package]]
 name = "requests"
 version = "2.31.0"
@@ -4301,6 +4761,25 @@ postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
 sqlcipher = ["sqlcipher3_binary"]
 
+[[package]]
+name = "stack-data"
+version = "0.6.3"
+description = "Extract data from python stack frames and tracebacks for informative displays"
+optional = false
+python-versions = "*"
+files = [
+    {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"},
+    {file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"},
+]
+
+[package.dependencies]
+asttokens = ">=2.1.0"
+executing = ">=1.2.0"
+pure-eval = "*"
+
+[package.extras]
+tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
+
 [[package]]
 name = "stevedore"
 version = "5.2.0"
@@ -4656,6 +5135,26 @@ torch = "2.2.2"
 [package.extras]
 scipy = ["scipy"]
 
+[[package]]
+name = "tornado"
+version = "6.4"
+description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+optional = false
+python-versions = ">= 3.8"
+files = [
+    {file = "tornado-6.4-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0"},
+    {file = "tornado-6.4-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263"},
+    {file = "tornado-6.4-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f7894c581ecdcf91666a0912f18ce5e757213999e183ebfc2c3fdbf4d5bd764e"},
+    {file = "tornado-6.4-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e43bc2e5370a6a8e413e1e1cd0c91bedc5bd62a74a532371042a18ef19e10579"},
+    {file = "tornado-6.4-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212"},
+    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2"},
+    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:88b84956273fbd73420e6d4b8d5ccbe913c65d31351b4c004ae362eba06e1f78"},
+    {file = "tornado-6.4-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:71ddfc23a0e03ef2df1c1397d859868d158c8276a0603b96cf86892bff58149f"},
+    {file = "tornado-6.4-cp38-abi3-win32.whl", hash = "sha256:6f8a6c77900f5ae93d8b4ae1196472d0ccc2775cc1dfdc9e7727889145c45052"},
+    {file = "tornado-6.4-cp38-abi3-win_amd64.whl", hash = "sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63"},
+    {file = "tornado-6.4.tar.gz", hash = "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee"},
+]
+
 [[package]]
 name = "tqdm"
 version = "4.66.4"
@@ -4676,6 +5175,21 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "traitlets"
+version = "5.14.3"
+description = "Traitlets Python configuration system"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
+    {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
+]
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
+
 [[package]]
 name = "triton"
 version = "2.2.0"
@@ -5035,4 +5549,4 @@ vision = ["albumentations", "gdown", "imagecorruptions", "kornia", "pycocotools"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9 <3.12"
-content-hash = "f063dc8a179414ce47272aa890facfcfcc6caaa86168fac4b539e5e637ac9e65"
+content-hash = "3db3cf9e456bd85357f3fbda947359320fb931e25752685872e20418fb4d298e"
diff --git a/pyproject.toml b/pyproject.toml
index 6057d05f..5d1d157a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -102,6 +102,7 @@ hannah-nas-eval = "hannah.nas.eval.__main__:main"
 [tool.poetry.group.dev.dependencies]
 pyre-check = "^0.9.17"
 pytest-xdist = "^3.1.0"
+ipykernel = "^6.29.4"
 
 
 [build-system]
diff --git a/test/test_graph_conversion.py b/test/test_graph_conversion.py
index 6c4f2820..58946744 100644
--- a/test/test_graph_conversion.py
+++ b/test/test_graph_conversion.py
@@ -87,7 +87,7 @@ def test_graph_conversion_lazy_convnet():
 
 
 def test_graph_conversion_functional_operators():
-    from hannah.models.embedded_vision_net.models import search_space
+    from hannah.models.embedded_vision_net.models import embedded_vision_net
     from hannah.nas.functional_operators.executor import BasicExecutor
     import hannah.nas.functional_operators.operators
 
@@ -97,7 +97,7 @@ def test_graph_conversion_functional_operators():
 
     # space = test_net(input)
 
-    space = search_space("net", input, num_classes=10)
+    space = embedded_vision_net("net", input, num_classes=10)
     # space.sample()
     model = BasicExecutor(space)
     model.initialize()
diff --git a/test/test_symbolic_metrics.py b/test/test_symbolic_metrics.py
index 4f45ea0f..1e17aaeb 100644
--- a/test/test_symbolic_metrics.py
+++ b/test/test_symbolic_metrics.py
@@ -1,6 +1,6 @@
 from omegaconf import OmegaConf
 import torch
-from hannah.models.embedded_vision_net.models import search_space
+from hannah.models.embedded_vision_net.models import embedded_vision_net
 from hannah.nas.functional_operators.executor import BasicExecutor
 from hannah.nas.functional_operators.op import Tensor
 from hannah.callbacks.summaries import FxMACSummaryCallback
@@ -597,7 +597,7 @@ def __init__(self, model, device, example_feature_array) -> None:
     input_shape = [1, 3, 32, 32]
     input = Tensor(name="input", shape=input_shape, axis=("N", "C", "H", "W"))
     cons = OmegaConf.create([{"name": "weights"}, {"name": "macs"}])
-    space = search_space(name="evn", input=input, num_classes=10, constraints=cons)
+    space = embedded_vision_net(name="evn", input=input, num_classes=10, constraints=cons)
     set_parametrization(PARAMS, space.parametrization())
 
     model = BasicExecutor(space)
diff --git a/test/test_weight_expression.py b/test/test_weight_expression.py
index 91ff25b3..3f9a6425 100644
--- a/test/test_weight_expression.py
+++ b/test/test_weight_expression.py
@@ -1,7 +1,7 @@
 import torch
 from hannah.callbacks.summaries import FxMACSummaryCallback
 from hannah.models.embedded_vision_net.expressions import expr_product, expr_sum
-from hannah.models.embedded_vision_net.models import search_space
+from hannah.models.embedded_vision_net.models import embedded_vision_net
 from hannah.nas.expressions.choice import Choice
 from hannah.nas.functional_operators.executor import BasicExecutor
 from hannah.nas.functional_operators.op import ChoiceOp, Tensor, Op
@@ -129,7 +129,7 @@ def __init__(self, model, device, example_feature_array) -> None:
             self.example_feature_array = example_feature_array
 
     input = Tensor(name="input", shape=(1, 3, 32, 32), axis=("N", "C", "H", "W"))
-    space = search_space(name="evn", input=input, num_classes=10)
+    space = embedded_vision_net(name="evn", input=input, num_classes=10)
 
     # find a valid config
     while True: