chore: fix make docs

docs/developer/support_new_onnx_node.md

@@ -4,19 +4,19 @@ Concrete ML supports a wide range of models through the integration of ONNX node
 
 ## Operator Implementation
 
-### [`ops_impl.py`](../../src/concrete/ml/onnx/ops_impl.py)
+### Numpy implementation
 
-This file is responsible for implementing the computation of ONNX operators using floating-point arithmetic. The implementation should mirror the behavior of the corresponding ONNX operator precisely. This includes adhering to the expected inputs, outputs, and operational semantics.
+The [`ops_impl.py`](../../src/concrete/ml/onnx/ops_impl.py) file is responsible for implementing the computation of ONNX operators using floating-point arithmetic. The implementation should mirror the behavior of the corresponding ONNX operator precisely. This includes adhering to the expected inputs, outputs, and operational semantics.
 
 Refer to the [ONNX documentation](https://github.com/onnx/onnx/blob/main/docs/Operators.md) to grasp the expected behavior, inputs and outputs of the operator.
 
-### [`onnx_utils.py`](../../src/concrete/ml/onnx/onnx_utils.py)
+### Operator Mapping
 
-After implementing the operator in `ops_impl.py`, you need to import it into `onnx_utils.py` and map it within the `ONNX_OPS_TO_NUMPY_IMPL` dictionary. This mapping is crucial for the framework to recognize and utilize the new operator.
+After implementing the operator in [`ops_impl.py`](../../src/concrete/ml/onnx/ops_impl.py), you need to import it into [`onnx_utils.py`](../../src/concrete/ml/onnx/onnx_utils.py) and map it within the `ONNX_OPS_TO_NUMPY_IMPL` dictionary. This mapping is crucial for the framework to recognize and utilize the new operator.
 
-## [`quantized_ops.py`](../../src/concrete/ml/quantization/quantized_ops.py)
+### Quantized Operator
 
-Quantized operators handle integer arithmetic and its implementation is required for the new ONNX to be executed in FHE.
+Quantized operators are defined in [`quantized_ops.py`](../../src/concrete/ml/quantization/quantized_ops.py) and are used to handle integer arithmetic. Their implementation is required for the new ONNX to be executed in FHE.
 
 There exist two types of quantized operators: