chore: fixing broken links

deps_licenses/licenses_linux_user.txt.md5

@@ -1 +1 @@
-MD5(poetry.lock)= c9ca01eaefec2ac0031a3f64bd35c149
+MD5(poetry.lock)= 072c41b2b9369e35cb4c3cb173ef4faa

deps_licenses/licenses_mac_user.txt.md5

@@ -1 +1 @@
-MD5(poetry.lock)= c9ca01eaefec2ac0031a3f64bd35c149
+MD5(poetry.lock)= 072c41b2b9369e35cb4c3cb173ef4faa

docs/advanced-topics/compilation.md

@@ -1,6 +1,6 @@
 # Compilation
 
-Concrete-ML implements machine model inference using Concrete-Numpy as a backend. In order to execute in FHE, a numerical program written in Concrete-Numpy needs to be compiled. This functionality is [described here](https://docs.zama.ai/concrete-numpy/getting-started/compiling_and_executing), and Concrete-ML hides away most of the complexity of this step. The entire compilation process is done by Concrete-Numpy.
+Concrete-ML implements machine model inference using Concrete-Numpy as a backend. In order to execute in FHE, a numerical program written in Concrete-Numpy needs to be compiled. This functionality is [described here](https://docs.zama.ai/concrete-numpy/getting-started/quick_start), and Concrete-ML hides away most of the complexity of this step. The entire compilation process is done by Concrete-Numpy.
 
 From the perspective of the Concrete-ML user, the compilation process performed by Concrete-Numpy can be broken up into 3 steps:
 
@@ -12,7 +12,7 @@ Additionally, the [client/server API](client_server.md) packages the result of t
 
 ## **Concrete-Numpy** op-graphs and the Virtual Library
 
-The first step in the list above takes a python function implemented using the Concrete-Numpy [supported operation set](https://docs.zama.ai/concrete-numpy/getting-started/numpy_support) and transforms it into an executable operation graph. In this step all the floating point subgraphs in the op-graph are fused and converted to Table Lookup operations.
+The first step in the list above takes a python function implemented using the Concrete-Numpy [supported operation set](https://docs.zama.ai/concrete-numpy/getting-started/compatibility) and transforms it into an executable operation graph. In this step all the floating point subgraphs in the op-graph are fused and converted to Table Lookup operations.
 
 This enables to:
 

docs/advanced-topics/quantization.md

@@ -135,7 +135,7 @@ def dequantize_output(self, qvalues: numpy.ndarray) -> numpy.ndarray:
 
 Intermediary values computed during model inference might need to be re-scaled into the quantized domain of a subsequent model operator. For example, the output of a convolution layer in a neural network might have values that are 8 bits wide, with the next convolutional layer requiring that its inputs are at most 2 bits wide. In the non-encrypted realm, this implies that we need to make use of floating point operations. To make this work with integers as required by FHE, Concrete-ML uses a table lookup (TLU), which is a [way to encode univariate functions in FHE](https://docs.zama.ai/concrete-numpy/tutorials/table_lookup). Table lookups are expensive in FHE, and so should only be used when necessary.
 
-The operations done by the activation function of a previous layer and additional re-scaling to the new quantized domain, which are all floating point operations, [can be fused to a single TLU](https://docs.zama.ai/concrete-numpy/tutorials/working_with_floating_points). Concrete-ML implements quantized operators that perform this fusion, significantly reducing the number of TLUs necessary to perform inference.
+The operations done by the activation function of a previous layer and additional re-scaling to the new quantized domain, which are all floating point operations, [can be fused to a single TLU](https://docs.zama.ai/concrete-numpy/tutorials/floating_points). Concrete-ML implements quantized operators that perform this fusion, significantly reducing the number of TLUs necessary to perform inference.
 
 There are 3 types of operators:
 

docs/developer-guide/api.md

@@ -1,4 +1,3 @@
 # API
 
-Look at [this](https://www.conventionalcommits.org/en/v1.0.0/)
-Look at [that](../autogenerated/pdoc.pdf)
+The APIs of the project are detailed in a [pdf](../autogenerated/pdoc.pdf).

script/doc_utils/api_doc_pdf.sh

@@ -12,7 +12,7 @@ do
             ;;
 
         "-h" | "--help")
-            echo "Usage: " $0
+            echo "Usage: $0"
             exit 0
             ;;
 
@@ -21,24 +21,23 @@ do
             ;;
         *)
             echo "Unknown param : $1"
-            exit -1
+            exit 255
             ;;
    esac
    shift
 done
 
 OUTPUT_DIR_PDF="docs/autogenerated/"
-PDOC=pdoc3
 
 # Generate in pdf
 rm -rf $OUTPUT_DIR_PDF
 mkdir -p $OUTPUT_DIR_PDF
-$PDOC --pdf src.concrete.ml > tmp.pdoc.output.txt 2>/dev/null
+poetry run pdoc3 --pdf src.concrete.ml > tmp.pdoc.output.txt 2>/dev/null
 
 # Problems of version
-PANDOC_VERSION_IS_NOT_2=`pandoc --version | grep "pandoc 2." > /dev/null; echo $?`
+PANDOC_VERSION_IS_NOT_2=$(pandoc --version | grep "pandoc 2." > /dev/null; echo $?)
 
-if [ $PANDOC_VERSION_IS_NOT_2 -eq 0 ]
+if [ "$PANDOC_VERSION_IS_NOT_2" -eq 0 ]
 then
   ENGINE="--pdf-engine"
 else

script/make_utils/setup_os_deps.sh

@@ -135,6 +135,7 @@ if [[ "${OS_NAME}" == "Linux" ]]; then
         pandoc \
         openssl \
         shellcheck \
+        texlive-latex-base texlive-latex-extra texlive-fonts-recommended texlive-xetex lmodern \
         wget && \
         ${CLEAR_APT_LISTS:+$CLEAR_APT_LISTS} \
         pip install --no-cache-dir --upgrade pip && \