Some formatting fix

Author: pobonomo

ONNX_EXPORT_GUIDE.md

@@ -8,7 +8,7 @@ Gurobi ML supports two types of neural network architectures:
 
 1. **Sequential Models**: Linear chains of layers (Dense → ReLU → Dense → ...)
    - Supported directly via `add_keras_constr` and `add_sequential_constr`
-   
+
 2. **DAG Models**: Arbitrary directed acyclic graphs with skip/residual connections
    - Supported via ONNX export + `add_onnx_dag_constr`
 
@@ -105,14 +105,14 @@ class ResidualBlock(nn.Module):
         self.linear1 = nn.Linear(dim, dim)
         self.relu = nn.ReLU()
         self.linear2 = nn.Linear(dim, dim)
-    
+
     def forward(self, x):
         identity = x  # Skip connection
-        
+
         out = self.linear1(x)
         out = self.relu(out)
         out = self.linear2(out)
-        
+
         out = out + identity  # Residual addition
         return out
 
@@ -122,7 +122,7 @@ class ResNetModel(nn.Module):
         self.input_layer = nn.Linear(4, 8)
         self.residual_block = ResidualBlock(8)
         self.output_layer = nn.Linear(8, 2)
-    
+
     def forward(self, x):
         x = self.input_layer(x)
         x = self.residual_block(x)  # Residual connection inside

src/gurobi_ml/onnx/__init__.py

@@ -18,18 +18,18 @@
 Provides two implementations:
 
 1. **add_onnx_constr**: Sequential feed-forward networks only
-   
+
    - Validates that models have sequential topology
    - Rejects models with skip/residual connections
    - Use for simple feed-forward networks
 
 2. **add_onnx_dag_constr**: Supports arbitrary DAG topologies
-   
+
    - Skip connections (input used by multiple layers)
    - Residual connections (intermediate outputs reused)
    - Multi-branch architectures
    - Any valid directed acyclic graph
-   
+
 **Recommended Workflow for Keras and PyTorch Models:**
 
 For models with complex architectures (ResNet, skip connections, etc.),

src/gurobi_ml/onnx/onnx_dag_model.py

@@ -141,7 +141,7 @@ def _topological_sort(self, graph):
         -------
         list of onnx.NodeProto
             Nodes in topological order
-            
+
         Raises
         ------
         NoModel

src/gurobi_ml/torch/sequential.py

@@ -64,29 +64,29 @@ def add_sequential_constr(
     Notes
     -----
     |VariablesDimensionsWarn|
-    
+
     **Models with Skip Connections or Residual Architectures**
-    
+
     This function only supports :external+torch:py:class:`torch.nn.Sequential` models
     (linear chains of layers). For models with more complex architectures such as:
-    
+
     - Skip connections (input reused by multiple layers)
     - Residual connections (intermediate outputs reused)
     - Custom :external+torch:py:class:`torch.nn.Module` with complex forward() logic
     - ResNet, DenseNet, or other modern architectures
-    
+
     **Use the ONNX export workflow instead:**
-    
+
     1. Export your PyTorch model to ONNX format:
-    
+
        .. code-block:: python
-       
+
            import torch
            import onnx
-           
+
            # Prepare dummy input with correct shape
            dummy_input = torch.randn(1, input_dim)
-           
+
            # Export to ONNX
            torch.onnx.export(
                pytorch_model,           # Your model
@@ -102,23 +102,23 @@ def add_sequential_constr(
                    'output': {0: 'batch_size'}
                }
            )
-           
+
            # Verify the exported model
            onnx_model = onnx.load("model.onnx")
            onnx.checker.check_model(onnx_model)
-    
+
     2. Load and use with Gurobi ML's ONNX DAG support:
-    
+
        .. code-block:: python
-       
+
            import onnx
            from gurobi_ml.onnx import add_onnx_dag_constr
-           
+
            onnx_model = onnx.load("model.onnx")
            pred = add_onnx_dag_constr(gp_model, onnx_model, input_vars)
-    
+
     For more details on exporting PyTorch models to ONNX, see:
-    
+
     - `PyTorch ONNX export tutorial <https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html>`_
     - `torch.onnx documentation <https://pytorch.org/docs/stable/onnx.html>`_
     - `ONNX tutorials <https://onnx.ai/get-started.html>`_