[Code generation] Added some of the missing layer parameters (WMEM, TMEM and API) to get_layer_parameters()

[Code generation] Added function to determine the number of FIFOs and intergrated it into the generation of the stream declarations to help writing the parameters as variables into the commands

[Code generation] Rename code_gen dictionary keys and copied new finn-hls-onnx-model with quantization_annotation to data path

[Test] Test now runs smoothly because of merge of streamlining branch into dev, also corrected the datatype to capital letters

[Test] Added setting of the tensor datatypes in test file, does not correspond to modelwrapper.py version yet

[Code Generation] Added computation commands function => code_gen.py returns now a dictionary with stream declarations, stream pragma statements and computation commands

[Code Generation] Changed get_layer_attribute function so that it now takes a single node as input, started to work on the function for the computation commands

[Code Generation] Added stream declaration to output dictionary and [Test] run pre-commit so little changes in tab size and spacing

[Test] Added unit test for code generation (at the moment it only calls the function code_gen with the finn-hls-model in the data folder) and [Code Generation] Added function to extract the layer attributes

[Test] removed onnx graph that was produced during the run-docker.sh and deleted an obsolete import in infer_shapes.py

[Test] Created new branch and python script for manually construction of onnx graph based on an example of the finn hls library, Deleted obsolete mixed_model.onnx in data path

unfortunately some bugs in onnxruntime made the previous approach
crash with certain test topologies, so we're opting for this
instead.

dim restriction: mul must be scalar or 1d
val restriction: mul value must be non-negative

[Test] Changed verification method to a check if all tensor shapes are set and the output of the new node has specific value