refactor: remove ml related functionality, small bug fixes

docs/dev/explanation/compilation.md

@@ -53,22 +53,6 @@ Here is the visual representation of the pipeline:
 
 ![Frontend Flow](../../_static/compilation-pipeline/frontend_flow.svg)
 
-## Overview of the torch compilation process
-
-Compiling a torch Module is pretty straightforward.
-
-The torch Module is first converted to a Numpy equivalent we call `NumpyModule` if all the layers in the torch Module are supported.
-
-Then the module is quantized post-training to be compatible with our compiler which only works on integers. The post training quantization uses the provided dataset for calibration.
-
-The dataset is then quantized to be usable for compilation with the QuantizedModule.
-
-The QuantizedModule is compiled yielding an executable FHECircuit.
-
-Here is the visual representation of the different steps:
-
-![Torch compilation flow](../../_static/compilation-pipeline/torch_to_numpy_flow.svg)
-
 ## Tracing
 
 Given a Python function `f` such as this one,

docs/dev/explanation/terminology_and_structure.md

@@ -45,13 +45,3 @@ In this section, we will discuss the module structure of **concrete-numpy** brie
         - np_inputset_helpers: utilities for inputsets
         - np_mlir_converter: utilities for MLIR conversion
         - tracing: tracing of numpy functions
-    - quantization: tools to quantize networks
-        - post_training: post training quantization
-        - quantized_activations: management of quantization in activations
-        - quantized_array: utilities for quantization
-        - quantized_layers: management of quantization of neural network layers
-        - quantized_module: main API for quantization
-    - torch: torch compilation and conversion
-        - compile: compilation of a torch module, including quantization
-        - numpy_module: conversion tools to turn a torch module into a numpy function
-