QONNX ops and new general transformations

Hi, this PR primarily adds support and documentation for the three QONNX ops:

Furthermore the PR adds basic ONNX transformations, which are required for the QONNX ingestion in FINN.

ExtractBiasFromConv
- Extracts the (optional) Bias from a Conv node and inserts it behind Conv node as an Add node.
GemmToMatMul
- Converts Gemm nodes into a MatMul and an Add node.
remove_node_and_rewire
- This got converted to a publicly facing function and I added support for multiple successors.
RemoveIdentityOps
- Got moved over from FINN.
RemoveEmptyPadding
- Removes padding nodes, which don't pad. This node is inserted by the BrevitasONNXManager during the export of global average layers.

A few tests got added as well:

test_brevitas_quant_onnx_export_and_exec
- This tests the execution of QONNX ops. However it should likely move into FINN, since the test intdroduces a dependency on PyTorch for the testing.
test_remove_identity_ops
- Got moved over from FINN.

Open ToDos:

[x] Move test_brevitas_quant_onnx_export_and_exec to FINN to remove the PyTorch dependency for QONNX op test.
[x] Rework MultiThreshold datatype inference.
- Issue with current implementation: The node currently applies the data type of the out_dtype field to the output tensor. This leads to rounding errors during execution if the scale and/or bias are not scalar.
- Suggested change: Keep the out_dtype as is, since it may be used by down-stream tools to read the internal datatype of the MultiThreshold node, then infer the output datatype from the out_dtype, scale and bias on demand.

Xilinx / finn-base