diff --git a/.flake8 b/.flake8
deleted file mode 100644
index 8dc03e0..0000000
--- a/.flake8
+++ /dev/null
@@ -1,3 +0,0 @@
-[flake8]
-max-line-length = 88
-# extend-ignore = F401
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 6769e21..b989dc0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -49,6 +49,7 @@ coverage.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
+.ruff_cache/
 cover/
 
 # Translations
diff --git a/pyproject.toml b/pyproject.toml
index 3bbeb48..e4cd3be 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -43,7 +43,8 @@ repository = "https://github.com/mert-kurttutan/torchview"
 # Scan the project directory with the default parameters
 packages = [
     'torchview',
-    'torchview.computation_node'
+    'torchview.computation_node',
+    'torchview.experimental',
 ]  
 
 [tool.setuptools.package-data]
diff --git a/torchview/experimental/__init__.py b/torchview/experimental/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/torchview/experimental/watch_graph.py b/torchview/experimental/watch_graph.py
new file mode 100644
index 0000000..1e5023d
--- /dev/null
+++ b/torchview/experimental/watch_graph.py
@@ -0,0 +1,165 @@
+from __future__ import annotations
+
+import graphviz
+
+from ..computation_graph import ComputationGraph
+from ..recorder_tensor import (
+    module_forward_wrapper, _orig_module_forward, Recorder
+
+)
+
+# TODO: test/change api to include also function calls, not only pytorch models
+# so, keep the api here as general as possible
+
+
+def watch_graph(
+    graph_name: str = 'model',
+    depth: int | float = 3,
+    strict: bool = True,
+    expand_nested: bool = False,
+    graph_dir: str | None = None,
+    hide_module_functions: bool = True,
+    hide_inner_tensors: bool = True,
+    roll: bool = False,
+    show_shapes: bool = True,
+    filename: str | None = None,
+    directory: str = '.',
+) -> Recorder:
+    '''
+    This is Experimental Do not Rely on this. Further details will be explained
+    in the docs. Returns context in which to watch graph execution, no complete yet
+
+    1) Root nodes (usually tensor node for input tensors) which connect to all
+    the other nodes of computation graph of pytorch module recorded during forward
+    propagation.
+
+    2) graphviz.Digraph object that contains visual representation of computation
+    graph of pytorch module. This graph visual shows modules/ module hierarchy,
+    torch_functions, shapes and tensors recorded during forward prop, for examples
+    see documentation, and colab notebooks.
+
+
+    Args:
+
+        graph_name (str):
+            Name for graphviz.Digraph object. Also default name graphviz file
+            of Graph Visualization
+            Default: 'model'
+
+        depth (int):
+            Upper limit for depth of nodes to be shown in visualization.
+            Depth is measured how far is module/tensor inside the module hierarchy.
+            For instance, main module has depth=0, whereas submodule of main module
+            has depth=1, and so on.
+            Default: 3
+
+        strict (bool):
+            if true, graphviz visual does not allow multiple edges
+            between nodes. Mutiple edge occurs e.g. when there are tensors
+            from module node to module node and hiding those tensors
+            Default: True
+
+        expand_nested (bool):
+            if true, shows nested modules with dashed borders
+
+        graph_dir (str):
+            Sets the direction of visual graph
+            'TB' -> Top to Bottom
+            'LR' -> Left to Right
+            'BT' -> Bottom to Top
+            'RL' -> Right to Left
+            Default: None -> TB
+
+        hide_module_function (bool):
+            Determines whether to hide module torch_functions. Some
+            modules consist only of torch_functions (no submodule),
+            e.g. nn.Conv2d.
+            True => Dont include module functions in graphviz
+            False => Include modules function in graphviz
+            Default: True
+
+        hide_inner_tensors (bool):
+            Inner tensor is all the tensors of computation graph
+            but input and output tensors
+            True => Does not show inner tensors in graphviz
+            False => Shows inner tensors in graphviz
+            Default: True
+
+        roll (bool):
+            If true, rolls recursive modules.
+            Default: False
+
+        show_shapes (bool):
+            True => Show shape of tensor, input, and output
+            False => Dont show
+            Default: True
+
+        filename (str):
+            name of the file to store dot syntax representation and
+            image file of graphviz graph. Defaults to graph_name
+
+        directory (str):
+            directory in which to store graphviz output files.
+            Default: .
+
+    Returns:
+        ComputationGraph object that contains visualization of the input
+        pytorch model in the form of graphviz Digraph object
+    '''
+
+    if filename is None:
+        filename = f'{graph_name}.gv'
+
+    if graph_dir is None:
+        graph_dir = 'TB'
+
+    validate_user_params(
+        depth,
+    )
+
+    graph_attr = {
+        'ordering': 'in',
+        'rankdir': graph_dir,
+    }
+
+    # visual settings from torchviz
+    # seems to work visually well
+    node_attr = {
+        'style': 'filled',
+        'shape': 'plaintext',
+        'align': 'left',
+        'fontsize': '10',
+        'ranksep': '0.1',
+        'height': '0.2',
+        'fontname': 'Linux libertine',
+        'margin': '0',
+    }
+
+    edge_attr = {
+        'fontsize': '10',
+    }
+    visual_graph = graphviz.Digraph(
+        name=graph_name, engine='dot', strict=strict,
+        graph_attr=graph_attr, node_attr=node_attr, edge_attr=edge_attr,
+        directory=directory, filename=filename
+    )
+
+    input_nodes = 0
+    model_graph = ComputationGraph(
+        visual_graph, input_nodes, show_shapes, expand_nested,   # type: ignore[arg-type]
+        hide_inner_tensors, hide_module_functions, roll, depth
+    )
+
+    new_module_forward = module_forward_wrapper(model_graph)
+
+    return Recorder(_orig_module_forward, new_module_forward, model_graph)
+
+
+def validate_user_params(
+    depth: int | float,
+) -> None:
+    """Raise exceptions if the user's input is invalid."""
+    if depth < 0:
+        raise ValueError(
+            f"depth must be a non-negative number, depth={depth}"
+        )