diff --git a/.github/workflows/continuous-integration.yaml b/.github/workflows/continuous-integration.yaml
index 8de971e81..efdb22d98 100644
--- a/.github/workflows/continuous-integration.yaml
+++ b/.github/workflows/continuous-integration.yaml
@@ -21,13 +21,13 @@ on:
           - pr
 
       linux_python_versions:
-        description: "Space separated list of python versions (3.8, 3.9, 3.10, 3.11 are supported) to launch on linux"
+        description: "Space separated list of python versions (3.8, 3.9, 3.10, 3.11, 3.12 are supported) to launch on linux"
         required: false
         type: string
         default: "3.8"
 
       macos_python_versions:
-        description: "Space separated list of python versions (3.8, 3.9, 3.10, 3.11 are supported) to launch on macos (intel)"
+        description: "Space separated list of python versions (3.8, 3.9, 3.10, 3.11, 3.12 are supported) to launch on macos (intel)"
         required: false
         type: string
         default: "3.8"
@@ -165,6 +165,7 @@ jobs:
       needs-39-linux-runner: ${{ steps.set-matrix.outputs.needs-39-linux-runner }}
       needs-310-linux-runner: ${{ steps.set-matrix.outputs.needs-310-linux-runner }}
       needs-311-linux-runner: ${{ steps.set-matrix.outputs.needs-311-linux-runner }}
+      needs-312-linux-runner: ${{ steps.set-matrix.outputs.needs-312-linux-runner }}
       instance-type: ${{ steps.set-matrix.outputs.instance-type }}
       linux-python-versions: ${{ steps.set-matrix.outputs.linux-python-versions }}
       macos-python-versions: ${{ steps.set-matrix.outputs.macos-python-versions }}
@@ -213,10 +214,10 @@ jobs:
             LINUX_PYTHON_VERSIONS="3.8"
             MACOS_PYTHON_VERSIONS=""
           elif [[ "${BUILD_TYPE}" == "weekly" ]]; then
-            LINUX_PYTHON_VERSIONS="3.8 3.9 3.10 3.11"
+            LINUX_PYTHON_VERSIONS="3.8 3.9 3.10 3.11 3.12"
             MACOS_PYTHON_VERSIONS="3.9"
           elif [[ "${BUILD_TYPE}" == "release" ]] || [[ "${BUILD_TYPE}" == "published_release" ]]; then
-            LINUX_PYTHON_VERSIONS="3.8 3.9 3.10 3.11"
+            LINUX_PYTHON_VERSIONS="3.8 3.9 3.10 3.11 3.12"
             MACOS_PYTHON_VERSIONS=""
           elif [[ "${BUILD_TYPE}" == "push_to_main" ]]; then
             LINUX_PYTHON_VERSIONS="3.8"
@@ -266,6 +267,8 @@ jobs:
             jq -rc '. | map(select(.os_kind=="linux" and .python_version=="3.10")) | length > 0')
           NEEDS_LINUX_311_RUNNER=$(echo "${LINUX_MATRIX}" | \
             jq -rc '. | map(select(.os_kind=="linux" and .python_version=="3.11")) | length > 0')
+          NEEDS_LINUX_312_RUNNER=$(echo "${LINUX_MATRIX}" | \
+            jq -rc '. | map(select(.os_kind=="linux" and .python_version=="3.12")) | length > 0')
 
           echo "Needs Linux 3.8 runner:"
           echo "${NEEDS_LINUX_38_RUNNER}"
@@ -279,10 +282,14 @@ jobs:
           echo "Needs Linux 3.11 runner:"
           echo "${NEEDS_LINUX_311_RUNNER}"
 
+          echo "Needs Linux 3.12 runner:"
+          echo "${NEEDS_LINUX_312_RUNNER}"
+
           echo "needs-38-linux-runner=${NEEDS_LINUX_38_RUNNER}" >> $GITHUB_OUTPUT
           echo "needs-39-linux-runner=${NEEDS_LINUX_39_RUNNER}" >> $GITHUB_OUTPUT
           echo "needs-310-linux-runner=${NEEDS_LINUX_310_RUNNER}" >> $GITHUB_OUTPUT
           echo "needs-311-linux-runner=${NEEDS_LINUX_311_RUNNER}" >> $GITHUB_OUTPUT
+          echo "needs-312-linux-runner=${NEEDS_LINUX_312_RUNNER}" >> $GITHUB_OUTPUT
           echo "instance-type=${INSTANCE_TYPE}" >> $GITHUB_OUTPUT
 
   start-runner-linux:
@@ -299,6 +306,8 @@ jobs:
       ec2-instance-id-310: ${{ steps.start-ec2-runner-310.outputs.ec2-instance-id || '' }}
       label-311: ${{ steps.start-ec2-runner-311.outputs.label }}
       ec2-instance-id-311: ${{ steps.start-ec2-runner-311.outputs.ec2-instance-id || '' }}
+      label-312: ${{ steps.start-ec2-runner-312.outputs.label }}
+      ec2-instance-id-312: ${{ steps.start-ec2-runner-312.outputs.ec2-instance-id || '' }}
       matrix: ${{ steps.update-linux-matrix.outputs.linux-matrix }}
     steps:
       - name: Checkout Code
@@ -407,6 +416,29 @@ jobs:
               {"Key": "Team", "Value": "CML"}
             ]
 
+      - name: Start EC2 runner python 3.12
+        id: start-ec2-runner-312
+        if: ${{ !cancelled() && fromJSON(needs.matrix-preparation.outputs.needs-312-linux-runner) }}
+        uses: machulav/ec2-github-runner@fcfb31a5760dad1314a64a0e172b78ec6fc8a17e
+        with:
+          mode: start
+          github-token: ${{ secrets.EC2_RUNNER_BOT_TOKEN }}
+          ec2-image-id: ${{ secrets.AWS_EC2_AMI }}
+          ec2-instance-type: ${{ needs.matrix-preparation.outputs.instance-type }}
+          subnet-id: ${{ secrets.AWS_EC2_SUBNET_ID }}
+          security-group-id: ${{ secrets.AWS_EC2_SECURITY_GROUP_ID }}
+          aws-resource-tags: >
+            [
+              {"Key": "Name", "Value": "cml-ci-ec2-github-runner-py312"},
+              {"Key": "GitHubRepository", "Value": "${{ github.repository }}"},
+              {"Key": "Python version", "Value": "3.12"},
+              {"Key": "Actor", "Value": "${{ github.actor }}"},
+              {"Key": "Action", "Value": "${{ github.action }}"},
+              {"Key": "GitHash", "Value": "${{ github.sha }}"},
+              {"Key": "RefName", "Value": "${{ github.ref_name }}"},
+              {"Key": "RunId", "Value": "${{ github.run_id }}"},
+              {"Key": "Team", "Value": "CML"}
+            ]
 
       - name: Update Linux runs_on Matrix
         id: update-linux-matrix
@@ -421,6 +453,8 @@ jobs:
           '(. | map(select(.os_kind=="linux" and .python_version=="3.10") |= . + {"runs_on": "${{ steps.start-ec2-runner-310.outputs.label }}"}) )')
           MATRIX=$(echo "${MATRIX}" | jq -rc \
           '(. | map(select(.os_kind=="linux" and .python_version=="3.11") |= . + {"runs_on": "${{ steps.start-ec2-runner-311.outputs.label }}"}) )')
+          MATRIX=$(echo "${MATRIX}" | jq -rc \
+          '(. | map(select(.os_kind=="linux" and .python_version=="3.12") |= . + {"runs_on": "${{ steps.start-ec2-runner-312.outputs.label }}"}) )')
 
           echo "Updated matrix:"
           echo "${MATRIX}"
@@ -1058,6 +1092,15 @@ jobs:
           ec2-instance-id: ${{ needs.start-runner-linux.outputs.ec2-instance-id-311 }}
           mode: stop
 
+      - name: Stop EC2 runner python 3.12
+        uses: machulav/ec2-github-runner@fcfb31a5760dad1314a64a0e172b78ec6fc8a17e
+        if: ${{ always() && needs.start-runner-linux.outputs.ec2-instance-id-312 }}
+        with:
+          github-token: ${{ secrets.EC2_RUNNER_BOT_TOKEN }}
+          label: ${{ needs.start-runner-linux.outputs.label-312 }}
+          ec2-instance-id: ${{ needs.start-runner-linux.outputs.ec2-instance-id-312 }}
+          mode: stop
+
   build-macos-intel:
     name: Python ${{ matrix.python_version }} (macOS, intel)
     needs: [matrix-preparation]
diff --git a/Makefile b/Makefile
index b8f39c248..2d9a24d95 100644
--- a/Makefile
+++ b/Makefile
@@ -1,9 +1,16 @@
 SHELL:=$(shell /usr/bin/env which bash)
 
+DEV_DOCKER_PYTHON?=py38
+ifeq ($(DEV_DOCKER_PYTHON),py312)
+UBUNTU_BASE=24.10
+else 
+UBUNTU_BASE=20.04
+endif 
+
 DEV_DOCKER_IMG:=concrete-ml-dev
 DEV_DOCKERFILE:=docker/Dockerfile.dev
-DEV_CONTAINER_VENV_VOLUME:=concrete-ml-venv
-DEV_CONTAINER_CACHE_VOLUME:=concrete-ml-cache
+DEV_CONTAINER_VENV_VOLUME:=concrete-ml-venv-$(DEV_DOCKER_PYTHON)
+DEV_CONTAINER_CACHE_VOLUME:=concrete-ml-cache-$(DEV_DOCKER_PYTHON)
 DOCKER_VENV_PATH:="$${HOME}"/dev_venv/
 SRC_DIR:=src
 TEST?=tests
@@ -207,7 +214,7 @@ spcc_internal: $(SPCC_DEPS)
 .PHONY: pytest_internal # Run pytest
 pytest_internal:
 	poetry run pytest --version
-	poetry run pytest $(TEST) \
+	MKL_NUM_THREADS=4 OMP_NUM_THREADS=4 poetry run pytest $(TEST) \
 	-svv \
 	--count=$(COUNT) \
 	--randomly-dont-reorganize \
@@ -342,12 +349,12 @@ mypy_ci:
 
 .PHONY: docker_build # Build dev docker
 docker_build:
-	BUILD_ARGS=; \
+	BUILD_ARGS="--build-arg UBUNTU_BASE=$(UBUNTU_BASE) ";\
 	if [[ $$(uname) == "Linux" ]]; then \
-		BUILD_ARGS="--build-arg BUILD_UID=$$(id -u) --build-arg BUILD_GID=$$(id -g)"; \
+		BUILD_ARGS+="--build-arg BUILD_UID=$$(id -u) --build-arg BUILD_GID=$$(id -g)"; \
 	fi; \
 	DOCKER_BUILDKIT=1 docker build $${BUILD_ARGS:+$$BUILD_ARGS} \
-	--pull -t $(DEV_DOCKER_IMG) -f $(DEV_DOCKERFILE) .
+	--pull -t $(DEV_DOCKER_IMG):$(DEV_DOCKER_PYTHON) -f $(DEV_DOCKERFILE) .
 
 .PHONY: docker_rebuild # Rebuild docker
 docker_rebuild: docker_clean_volumes
@@ -356,7 +363,7 @@ docker_rebuild: docker_clean_volumes
 		BUILD_ARGS="--build-arg BUILD_UID=$$(id -u) --build-arg BUILD_GID=$$(id -g)"; \
 	fi; \
 	DOCKER_BUILDKIT=1 docker build $${BUILD_ARGS:+$$BUILD_ARGS} \
-	--pull --no-cache -t $(DEV_DOCKER_IMG) -f $(DEV_DOCKERFILE) .
+	--pull --no-cache -t $(DEV_DOCKER_IMG):$(DEV_DOCKER_PYTHON) -f $(DEV_DOCKERFILE) .
 
 .PHONY: docker_start # Launch docker
 docker_start:
@@ -371,7 +378,7 @@ docker_start:
 	--volume /"$$(pwd)":/src \
 	--volume $(DEV_CONTAINER_VENV_VOLUME):/home/dev_user/dev_venv \
 	--volume $(DEV_CONTAINER_CACHE_VOLUME):/home/dev_user/.cache \
-	$(DEV_DOCKER_IMG) || rm -f "$${EV_FILE}"
+	$(DEV_DOCKER_IMG):$(DEV_DOCKER_PYTHON) || rm -f "$${EV_FILE}"
 
 .PHONY: docker_build_and_start # Docker build and start
 docker_build_and_start: docker_build docker_start
@@ -642,7 +649,7 @@ docker_publish_measurements: docker_rebuild
 	docker run --volume /"$$(pwd)":/src \
 	--volume $(DEV_CONTAINER_VENV_VOLUME):/home/dev_user/dev_venv \
 	--volume $(DEV_CONTAINER_CACHE_VOLUME):/home/dev_user/.cache \
-	$(DEV_DOCKER_IMG) \
+	$(DEV_DOCKER_IMG):$(DEV_DOCKER_PYTHON) \
 	/bin/bash ./script/progress_tracker_utils/benchmark_and_publish_findings_in_docker.sh \
 	"$${LAUNCH_COMMAND}"
 
diff --git a/README.md b/README.md
index 9cd488eee..3269f2774 100644
--- a/README.md
+++ b/README.md
@@ -83,7 +83,7 @@ Depending on your OS, Concrete ML may be installed with Docker or with pip:
 |            macOS 11+ (Intel)            |         Yes         |       Yes        |
 | macOS 11+ (Apple Silicon: M1, M2, etc.) |     Coming soon     |       Yes        |
 
-Note: Concrete ML only supports Python `3.8`, `3.9`, `3.10` and `3.11`.
+Note: Concrete ML only supports Python `3.8`, `3.9`, `3.10`, `3.11` and `3.12`.
 Concrete ML can be installed on Kaggle ([see this question on the community for more details](https://community.zama.ai/t/how-do-we-use-concrete-ml-on-kaggle/332)) and on Google Colab.
 
 #### Docker
diff --git a/benchmarks/common.py b/benchmarks/common.py
index 691ccf19a..3d92d521b 100644
--- a/benchmarks/common.py
+++ b/benchmarks/common.py
@@ -757,6 +757,8 @@ def benchmark_name_generator(
 
     elif model_name in DEEP_LEARNING_NAMES:
         config_str = f"_{config['n_bits']}"
+    else:
+        config_str = "UNKNOWN"
 
     # We remove underscores to make sure to not have any conflict when splitting
     return model_name.replace("_", "-") + config_str + joiner + dataset_name.replace("_", "-")
diff --git a/benchmarks/glm.py b/benchmarks/glm.py
index a3df541d4..60ddee564 100644
--- a/benchmarks/glm.py
+++ b/benchmarks/glm.py
@@ -8,6 +8,7 @@
 import numpy as np
 import pandas
 import py_progress_tracker as progress
+import sklearn
 from common import (
     BENCHMARK_CONFIGURATION,
     GLMS_STRING_TO_CLASS,
@@ -29,6 +30,10 @@
     StandardScaler,
 )
 
+sklearn_sparse_arg = (
+    {"sparse": False} if "1.1." in sklearn.__version__ else {"sparse_output": False}
+)
+
 
 # pylint: disable=redefined-outer-name
 def get_data() -> Tuple[pandas.DataFrame, str]:
@@ -102,7 +107,7 @@ def get_preprocessor() -> ColumnTransformer:
             ("log_scaled_numeric", log_scale_transformer, ["Density"]),
             (
                 "onehot_categorical",
-                OneHotEncoder(sparse=False),
+                OneHotEncoder(**sklearn_sparse_arg),
                 ["VehBrand", "VehPower", "VehGas", "Region", "Area"],
             ),
         ],
diff --git a/deps_licenses/licenses_linux_user.txt b/deps_licenses/licenses_linux_user.txt
index d15531268..2a312fecc 100644
--- a/deps_licenses/licenses_linux_user.txt
+++ b/deps_licenses/licenses_linux_user.txt
@@ -7,22 +7,22 @@ certifi, 2024.8.30, Mozilla Public License 2.0 (MPL 2.0)
 charset-normalizer, 3.4.0, MIT License
 coloredlogs, 15.0.1, MIT License
 concrete-ml-extensions, 0.1.2, BSD-3-Clause-Clear
-concrete-python, 2.8.1, BSD-3-Clause
+concrete-python, 2.9.0rc1.dev20241130, BSD-3-Clause
 dependencies, 2.0.1, BSD License
 dill, 0.3.9, BSD License
 filelock, 3.16.1, The Unlicense (Unlicense)
-flatbuffers, 2.0.7, Apache Software License
-fsspec, 2024.9.0, BSD License
-huggingface-hub, 0.25.2, Apache Software License
+flatbuffers, 24.3.25, Apache Software License
+fsspec, 2024.10.0, BSD License
+huggingface-hub, 0.26.3, Apache Software License
 humanfriendly, 10.0, MIT License
 hummingbird-ml, 0.4.11, MIT License
 idna, 3.10, BSD License
 importlib_resources, 6.4.5, Apache Software License
 joblib, 1.4.2, BSD License
-jsonpickle, 3.3.0, BSD License
+jsonpickle, 4.0.0, BSD License
 mpmath, 1.3.0, BSD License
 networkx, 3.1, BSD License
-numpy, 1.24.2, BSD License
+numpy, 1.26.4, BSD License
 nvidia-cublas-cu12, 12.1.3.1, Other/Proprietary License
 nvidia-cuda-cupti-cu12, 12.1.105, Other/Proprietary License
 nvidia-cuda-nvrtc-cu12, 12.1.105, Other/Proprietary License
@@ -33,22 +33,22 @@ nvidia-curand-cu12, 10.3.2.106, Other/Proprietary License
 nvidia-cusolver-cu12, 11.4.5.107, Other/Proprietary License
 nvidia-cusparse-cu12, 12.1.0.106, Other/Proprietary License
 nvidia-nccl-cu12, 2.20.5, Other/Proprietary License
-nvidia-nvjitlink-cu12, 12.6.77, Other/Proprietary License
+nvidia-nvjitlink-cu12, 12.6.85, Other/Proprietary License
 nvidia-nvtx-cu12, 12.1.105, Other/Proprietary License
-onnx, 1.16.1, Apache License v2.0
+onnx, 1.17.0, Apache License v2.0
 onnxconverter-common, 1.13.0, MIT License
 onnxmltools, 1.12.0, Apache Software License
 onnxoptimizer, 0.3.13, Apache License v2.0
 onnxruntime, 1.18.0, MIT License
-packaging, 24.1, Apache Software License; BSD License
+packaging, 24.2, Apache Software License; BSD License
 pandas, 2.0.3, BSD License
-protobuf, 4.25.5, 3-Clause BSD License
+protobuf, 3.20.3, BSD-3-Clause
 psutil, 5.9.8, BSD License
 python-dateutil, 2.9.0.post0, Apache Software License; BSD License
 pytz, 2024.2, MIT License
 requests, 2.32.3, Apache Software License
-scikit-learn, 1.1.3, BSD License
-scipy, 1.10.1, BSD License
+scikit-learn, 1.4.0, BSD License
+scipy, 1.13.1, BSD License
 six, 1.16.0, MIT License
 skl2onnx, 1.17.0, Apache Software License
 skops, 0.5.0, MIT
@@ -57,7 +57,7 @@ sympy, 1.13.3, BSD License
 tabulate, 0.8.10, MIT License
 threadpoolctl, 3.5.0, BSD License
 torch, 2.3.1, BSD License
-tqdm, 4.66.5, MIT License; Mozilla Public License 2.0 (MPL 2.0)
+tqdm, 4.67.1, MIT License; Mozilla Public License 2.0 (MPL 2.0)
 triton, 2.3.1, MIT License
 typing_extensions, 4.12.2, Python Software Foundation License
 tzdata, 2024.2, Apache Software License
diff --git a/deps_licenses/licenses_linux_user.txt.md5 b/deps_licenses/licenses_linux_user.txt.md5
index c5f2bcbd8..4ad1891af 100644
--- a/deps_licenses/licenses_linux_user.txt.md5
+++ b/deps_licenses/licenses_linux_user.txt.md5
@@ -1 +1 @@
-8ea8aec4f5aac03565c2dcb9f3f8a1da
+85a519f8389929d8e87aa0367decff53
diff --git a/deps_licenses/licenses_mac_intel_user.txt b/deps_licenses/licenses_mac_intel_user.txt
index f3917dab5..79254ffe6 100644
--- a/deps_licenses/licenses_mac_intel_user.txt
+++ b/deps_licenses/licenses_mac_intel_user.txt
@@ -6,36 +6,36 @@ brevitas, 0.10.2, UNKNOWN
 certifi, 2024.8.30, Mozilla Public License 2.0 (MPL 2.0)
 charset-normalizer, 3.4.0, MIT License
 coloredlogs, 15.0.1, MIT License
-concrete-python, 2.8.1, BSD-3-Clause
+concrete-python, 2.9.0rc1.dev20241130, BSD-3-Clause
 dependencies, 2.0.1, BSD License
 dill, 0.3.9, BSD License
 filelock, 3.16.1, The Unlicense (Unlicense)
-flatbuffers, 2.0.7, Apache Software License
-fsspec, 2024.9.0, BSD License
-huggingface-hub, 0.25.2, Apache Software License
+flatbuffers, 24.3.25, Apache Software License
+fsspec, 2024.10.0, BSD License
+huggingface-hub, 0.26.3, Apache Software License
 humanfriendly, 10.0, MIT License
 hummingbird-ml, 0.4.11, MIT License
 idna, 3.10, BSD License
 importlib_resources, 6.4.5, Apache Software License
 joblib, 1.4.2, BSD License
-jsonpickle, 3.3.0, BSD License
+jsonpickle, 4.0.0, BSD License
 mpmath, 1.3.0, BSD License
 networkx, 3.1, BSD License
-numpy, 1.24.2, BSD License
-onnx, 1.16.1, Apache License v2.0
+numpy, 1.26.4, BSD License
+onnx, 1.17.0, Apache License v2.0
 onnxconverter-common, 1.13.0, MIT License
 onnxmltools, 1.12.0, Apache Software License
 onnxoptimizer, 0.3.13, Apache License v2.0
 onnxruntime, 1.18.0, MIT License
-packaging, 24.1, Apache Software License; BSD License
+packaging, 24.2, Apache Software License; BSD License
 pandas, 2.0.3, BSD License
-protobuf, 4.25.5, 3-Clause BSD License
+protobuf, 3.20.3, BSD-3-Clause
 psutil, 5.9.8, BSD License
 python-dateutil, 2.9.0.post0, Apache Software License; BSD License
 pytz, 2024.2, MIT License
 requests, 2.32.3, Apache Software License
-scikit-learn, 1.1.3, BSD License
-scipy, 1.10.1, BSD License
+scikit-learn, 1.4.0, BSD License
+scipy, 1.13.1, BSD License
 six, 1.16.0, MIT License
 skl2onnx, 1.17.0, Apache Software License
 skops, 0.5.0, MIT
@@ -44,7 +44,7 @@ sympy, 1.13.3, BSD License
 tabulate, 0.8.10, MIT License
 threadpoolctl, 3.5.0, BSD License
 torch, 2.2.2, BSD License
-tqdm, 4.66.5, MIT License; Mozilla Public License 2.0 (MPL 2.0)
+tqdm, 4.67.1, MIT License; Mozilla Public License 2.0 (MPL 2.0)
 typing_extensions, 4.12.2, Python Software Foundation License
 tzdata, 2024.2, Apache Software License
 unfoldNd, 0.2.2, MIT License
diff --git a/deps_licenses/licenses_mac_intel_user.txt.md5 b/deps_licenses/licenses_mac_intel_user.txt.md5
index c5f2bcbd8..4ad1891af 100644
--- a/deps_licenses/licenses_mac_intel_user.txt.md5
+++ b/deps_licenses/licenses_mac_intel_user.txt.md5
@@ -1 +1 @@
-8ea8aec4f5aac03565c2dcb9f3f8a1da
+85a519f8389929d8e87aa0367decff53
diff --git a/deps_licenses/licenses_mac_silicon_user.txt b/deps_licenses/licenses_mac_silicon_user.txt
index 36e4d389a..e4e68a8c1 100644
--- a/deps_licenses/licenses_mac_silicon_user.txt
+++ b/deps_licenses/licenses_mac_silicon_user.txt
@@ -6,36 +6,36 @@ brevitas, 0.10.2, UNKNOWN
 certifi, 2024.8.30, Mozilla Public License 2.0 (MPL 2.0)
 charset-normalizer, 3.4.0, MIT License
 coloredlogs, 15.0.1, MIT License
-concrete-python, 2.8.1, BSD-3-Clause
+concrete-python, 2.9.0rc1.dev20241130, BSD-3-Clause
 dependencies, 2.0.1, BSD License
 dill, 0.3.9, BSD License
 filelock, 3.16.1, The Unlicense (Unlicense)
-flatbuffers, 2.0.7, Apache Software License
-fsspec, 2024.9.0, BSD License
-huggingface-hub, 0.25.2, Apache Software License
+flatbuffers, 24.3.25, Apache Software License
+fsspec, 2024.10.0, BSD License
+huggingface-hub, 0.26.3, Apache Software License
 humanfriendly, 10.0, MIT License
 hummingbird-ml, 0.4.11, MIT License
 idna, 3.10, BSD License
 importlib_resources, 6.4.5, Apache Software License
 joblib, 1.4.2, BSD License
-jsonpickle, 3.3.0, BSD License
+jsonpickle, 4.0.0, BSD License
 mpmath, 1.3.0, BSD License
 networkx, 3.1, BSD License
-numpy, 1.24.2, BSD License
-onnx, 1.16.1, Apache License v2.0
+numpy, 1.26.4, BSD License
+onnx, 1.17.0, Apache License v2.0
 onnxconverter-common, 1.13.0, MIT License
 onnxmltools, 1.12.0, Apache Software License
 onnxoptimizer, 0.3.13, Apache License v2.0
 onnxruntime, 1.18.0, MIT License
-packaging, 24.1, Apache Software License; BSD License
+packaging, 24.2, Apache Software License; BSD License
 pandas, 2.0.3, BSD License
-protobuf, 4.25.5, 3-Clause BSD License
+protobuf, 3.20.3, BSD-3-Clause
 psutil, 5.9.8, BSD License
 python-dateutil, 2.9.0.post0, Apache Software License; BSD License
 pytz, 2024.2, MIT License
 requests, 2.32.3, Apache Software License
-scikit-learn, 1.1.3, BSD License
-scipy, 1.10.1, BSD License
+scikit-learn, 1.4.0, BSD License
+scipy, 1.13.1, BSD License
 six, 1.16.0, MIT License
 skl2onnx, 1.17.0, Apache Software License
 skops, 0.5.0, MIT
@@ -44,7 +44,7 @@ sympy, 1.13.3, BSD License
 tabulate, 0.8.10, MIT License
 threadpoolctl, 3.5.0, BSD License
 torch, 2.3.1, BSD License
-tqdm, 4.66.5, MIT License; Mozilla Public License 2.0 (MPL 2.0)
+tqdm, 4.67.1, MIT License; Mozilla Public License 2.0 (MPL 2.0)
 typing_extensions, 4.12.2, Python Software Foundation License
 tzdata, 2024.2, Apache Software License
 unfoldNd, 0.2.2, MIT License
diff --git a/deps_licenses/licenses_mac_silicon_user.txt.md5 b/deps_licenses/licenses_mac_silicon_user.txt.md5
index c5f2bcbd8..4ad1891af 100644
--- a/deps_licenses/licenses_mac_silicon_user.txt.md5
+++ b/deps_licenses/licenses_mac_silicon_user.txt.md5
@@ -1 +1 @@
-8ea8aec4f5aac03565c2dcb9f3f8a1da
+85a519f8389929d8e87aa0367decff53
diff --git a/docker/Dockerfile.dev b/docker/Dockerfile.dev
index ecffa09bf..49120c155 100644
--- a/docker/Dockerfile.dev
+++ b/docker/Dockerfile.dev
@@ -1,4 +1,5 @@
-FROM ubuntu:20.04
+ARG UBUNTU_BASE=20.04
+FROM ubuntu:${UBUNTU_BASE}
 
 ENV TZ=Europe/Paris
 RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
@@ -7,10 +8,6 @@ RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
 # original archive showed performance issues and is farther away
 RUN sed -i 's|^deb http://archive|deb http://fr.archive|g' /etc/apt/sources.list
 
-COPY ./script/make_utils/setup_os_deps.sh ./setup_os_deps.sh
-COPY ./script/make_utils/detect_docker.sh ./detect_docker.sh
-RUN ./setup_os_deps.sh --linux-install-python && rm ./setup_os_deps.sh && rm ./detect_docker.sh
-
 ENV SRC_DIR=/src
 
 # Default to Ubuntu default uid for first user
@@ -19,15 +16,20 @@ ARG BUILD_UID=1000
 
 # Get sudo and vim for our future user
 RUN apt-get update && \
-    apt-get install --no-install-recommends -y sudo vim && \
+    apt-get install --no-install-recommends -y sudo vim adduser && \
     rm -rf /var/lib/apt/lists/*
 
+# For ubuntu>23 the ubuntu user will exist and use uid/gid=1000
+# we need to remove this user if it exists
+RUN userdel -f -r ubuntu; exit 0
+
 # From https://dev.to/emmanuelnk/using-sudo-without-password-prompt-as-non-root-docker-user-52bg
 # Create dev_user and add it to relevant groups
 # Create /src and make the dev user own it
 # Ensure sudo group users are not asked for a password when using
 # sudo command by ammending sudoers file
-RUN groupadd -g "${BUILD_GID}" dev_user && \
+
+RUN groupadd -f -g "${BUILD_GID}" dev_user && \
     adduser --disabled-password \
     --uid "${BUILD_UID}" --gid "${BUILD_GID}" --shell /bin/bash --gecos "" dev_user && \
     usermod -aG sudo dev_user && \
@@ -35,9 +37,17 @@ RUN groupadd -g "${BUILD_GID}" dev_user && \
     chown dev_user "${SRC_DIR}" && \
     echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
 
+RUN groupadd docker && usermod -aG docker dev_user
+
 # Now switch to the newly created user
 USER dev_user
 
+RUN cd /home/dev_user
+
+COPY ./script/make_utils/setup_os_deps.sh ./setup_os_deps.sh
+COPY ./script/make_utils/detect_docker.sh ./detect_docker.sh
+RUN ./setup_os_deps.sh --linux-install-python && sudo rm ./setup_os_deps.sh && sudo rm ./detect_docker.sh
+
 RUN echo "source ~/dev_venv/bin/activate" >> ~/.bashrc && \
     echo "if [[ \"\$?\" != \"0\" ]]; then" >> ~/.bashrc && \
     echo "    python3 -m venv ~/dev_venv" >> ~/.bashrc && \
diff --git a/docker/release_resources/sanity_check.py b/docker/release_resources/sanity_check.py
index 535437584..911edb4dc 100644
--- a/docker/release_resources/sanity_check.py
+++ b/docker/release_resources/sanity_check.py
@@ -94,7 +94,7 @@ def ml_check(args, keyring_dir_as_str):
     print(f"Prediction sklearn       : {y_pred[:nb_samples]}")
 
     print(
-        f"{numpy.sum(y_pred_simulated==y_pred[:nb_samples])}/{nb_samples} "
+        f"{numpy.sum(y_pred_simulated == y_pred[:nb_samples])}/{nb_samples} "
         "predictions are similar between the FHE simulated model and the clear sklearn model."
     )
 
diff --git a/docs/advanced_examples/DecisionTreeClassifier.ipynb b/docs/advanced_examples/DecisionTreeClassifier.ipynb
index d7ffa485c..da5714ffb 100644
--- a/docs/advanced_examples/DecisionTreeClassifier.ipynb
+++ b/docs/advanced_examples/DecisionTreeClassifier.ipynb
@@ -319,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -332,7 +332,7 @@
    ],
    "source": [
     "print(\n",
-    "    f\"{numpy.sum(y_pred_fhe==y_pred)}/\"\n",
+    "    f\"{numpy.sum(y_pred_fhe == y_pred)}/\"\n",
     "    \"10 predictions are similar between the FHE model and the clear sklearn model.\"\n",
     ")"
    ]
diff --git a/docs/advanced_examples/GLMComparison.ipynb b/docs/advanced_examples/GLMComparison.ipynb
index b2a6171a1..f4567b28d 100644
--- a/docs/advanced_examples/GLMComparison.ipynb
+++ b/docs/advanced_examples/GLMComparison.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,6 +42,7 @@
     "from timeit import default_timer as timer\n",
     "\n",
     "import numpy as np\n",
+    "import sklearn\n",
     "from sklearn.compose import ColumnTransformer\n",
     "from sklearn.datasets import fetch_openml\n",
     "from sklearn.linear_model import GammaRegressor as SklearnGammaRegressor\n",
@@ -162,10 +163,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "sklearn_sparse_arg = (\n",
+    "    {\"sparse\": False} if \"1.1.\" in sklearn.__version__ else {\"sparse_output\": False}\n",
+    ")\n",
     "log_scale_transformer = make_pipeline(FunctionTransformer(np.log, validate=False), StandardScaler())\n",
     "\n",
     "linear_model_preprocessor = ColumnTransformer(\n",
@@ -175,7 +179,7 @@
     "        (\"log_scaled_numeric\", log_scale_transformer, [\"Density\"]),\n",
     "        (\n",
     "            \"onehot_categorical\",\n",
-    "            OneHotEncoder(sparse=False),\n",
+    "            OneHotEncoder(**sklearn_sparse_arg),\n",
     "            [\"VehBrand\", \"VehPower\", \"VehGas\", \"Region\", \"Area\"],\n",
     "        ),\n",
     "    ],\n",
diff --git a/docs/advanced_examples/LogisticRegressionTraining.ipynb b/docs/advanced_examples/LogisticRegressionTraining.ipynb
index fd1ea458b..ca9d7023b 100644
--- a/docs/advanced_examples/LogisticRegressionTraining.ipynb
+++ b/docs/advanced_examples/LogisticRegressionTraining.ipynb
@@ -466,7 +466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -515,7 +515,7 @@
     "    # Measure accuracy of the model with FHE simulation\n",
     "    y_pred_partial_fhe = model_concrete_partial.predict(x_test, fhe=\"simulate\")\n",
     "\n",
-    "    accuracy_partial = accuracy_score(y_test, y_pred_partial_fhe).mean()\n",
+    "    accuracy_partial = accuracy_score(y_test, y_pred_partial_fhe)\n",
     "    accuracy_scores.append(accuracy_partial)\n",
     "\n",
     "# Plot the evolution of accuracy throughout the training process\n",
diff --git a/docs/advanced_examples/PoissonRegression.ipynb b/docs/advanced_examples/PoissonRegression.ipynb
index 06fd4f34d..24df931c7 100644
--- a/docs/advanced_examples/PoissonRegression.ipynb
+++ b/docs/advanced_examples/PoissonRegression.ipynb
@@ -35,6 +35,7 @@
     "import time\n",
     "\n",
     "import numpy as np\n",
+    "import sklearn\n",
     "from sklearn.datasets import fetch_openml\n",
     "from sklearn.linear_model import PoissonRegressor as SklearnPoissonRegressor\n",
     "from sklearn.metrics import mean_poisson_deviance\n",
@@ -125,7 +126,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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yT0lbx0gntksVGkqNJknBEZ7HKW6e4iwN45vNvSeetomv4jHb7onZ9TLT3+z7srjbPcXur8+JGaUhRsl/cZaW/AAAAAAo9SisBbq0BdKWEVLWn/+0hdWWIltIBxdLyv2nfdtoKaKedGrHP21Hlkl7bFJkvP1L5rnjNJ4qxfYsvvi3PiTtmuwaZ71kqdHEwB7fbO49zbmxh3Two3+e52+T6t2lFouKHk/N/tL+dwvf7mmbm82lmf6ecuDpfWl2ncy2e9qG1W+WMjeW/OfEDE/vg0CKUfJfnKUlPwAAAADKBE4FDWRpC6RNvZ2/IEr25wc/klNRQLI/P7uodrbMFDfj7LWPn7bAVxE72/qQtGuS3Ma5a5J9eaCO703u3c15bkHpbAc/si8vajy7Jplo97DNzebSTP+CcuD2fWl2nbxo97QND35U8p8TMzy+DwIoRsl/cZaW/AAAAAAoMyisBSoj137UhYzinMT+z9aR9vl8KS/776OZCrBrsr1foI1flNyfPWfuKc8FpXwHP7L3K654XAez/3P2NjebSzP9C5ODgFeMnxNTYRT0PgiQGCX/xVla8gMAAACgTKGwFqiOfO161EWxMKTTe+zz+dIf0+R6RNC5cv/uF2DjFyn3Z825dUzhXnK+fj5/L5yzzc3m0kz/wuYg4BXT58SM874PAiBGyX9xlpb8AAAAAChTKKwFqqz9pXu+kx5OSfW2X0mOX9Rc5M95Ynvh+p+vX3G9F/LHNZtLM/0Lm4PSoqQ/l97M7c8Yzczv6zhLS34AlBibzaa4uDjFx8f7OxQAAFCGUVgLVGE1S/d85Rv4tl9Jjl/UXOTPWaFh4fqfr19xvRfyxzWbSzP9C5uD0qKkP5fezO3PGM3M7+s4S0t+AJQYq9Wq1NRUpaSk+DsUAABQhlFYC1TR7ex3spOlmCeySOF17PP5Ut2hkoLP0yn4734BNn6Rcn/WnI0mFe4l5+vn8/fCOdvcbC7N9C9sDgJeMX1OzDjv+yAAYpT8F2dpyQ8AAACAMoXCWqCyBEuNp+Y/Ka5J7P80mmKfz5eCQqV6yQX3qZds7xdo4xcl92fPGRwhVe9ecP/q3e39iise18Hs/5y9zc3m0kz/wuQg4BXj58RUGAW9DwIkRsl/cZaW/AAAAAAoUyisBbLYnlLz+VLYRc7t4XX+Llac+wUxWIrwcJpeZPzfR3OcPU5t+/ixPX0VsbNGE6V6Y+Q2znpj7MsDdXxvcu9uzhaLPBeWqne3Ly9qPPXGuNm2nto9bHOzuTTTv6AcuH1fml0nL9o9bcPq3Uv+c2KGx/dBAMUo+S/O0pIfAAAAAGVGOX8HgPOI7SnFdP/7jnf77dcHim5nP+oiL9t+58WTO+zXs6o71H6UUO4p+90YT2y3X+Oq0ST7kUNGrvtxilOjidJlT7mPM9DH9yb37rRY5Hmb+CqeyyeYa3fHbC7N9C8oB57el2bXyWy7p23oj8+JGQW9DwKJv+IsLfkBAAAAUCZQWCsNLMFS1fau7UGhUv2Rru3BEVKTlws/TnHzFGdpGN9s7j3xtE18FY/Zdk/MrpeZ/mbfl8Xd7il2f31OzCgNMUr+i7O05AcAAABAqcepoAAAAAAAAIAXKKwBAACgzLHZbIqLi1N8fLy/QwEAAGUYp4KWZp6uBXXmuPTjgH+uHdXsbalcRc/9zV77ysz1xQriaRyz7Z7id5eHvGzp2+ulrH1SWC2p9ZdSaLTnMcxehysnQ9rQTTq9Wwq/WGr5qRQS5XkcT/09bUOzcWYdlNa1krIPSqHVpYTvpLDq5re5pzjdjZOX7b6vmTGCIzzH7mmc7CPut63ZbWj2/Vec4xi55j5rvro+nK9yYPbagmbj99TfzDiBfk09AF6zWq2yWq3KzMxUVFSUv8MBAABllMUwDMNfk3/11VeaNGmSNm7cqP3792vhwoXq0aOHY7lhGHr88cf12muv6ejRo7r22mv1yiuvqGHDho4+R44c0fDhw/XJJ58oKChIvXr10tSpU1WxYsVCx5G/w5WRkaHIyEhfrmLxSVsgbRkhZf35T1tYbSkoTDq1w7V/RAMpL8u1f1hNKTPFtX9kvP2L5rn9I1tIBxdLyj2rc7BUL9ncXTi3PiTtmuw6TuTVUub3hW+PqOd+fctFSWcyCheLJVQysl3bIxpIp3a5zln9Zilzo2tujBwpO91DLMddxylX0X2MnuLxtE6e4rQEux9H5SSdcW32tM09rVdQeSnvpJvx3fEwp8cxPPSXRZK7H1lBkvI8jJ+lQm9DT+9vT++/4hwnuKKUe/Kc9Srgs+bpZ0Ljqebuhmn2s+kpB55+tni6G67Z+D31r9lf2v9u4cbxVc5wwSqV+w8XoOLeTjk5Ofrss8/Uv39/nTp1yufjl4Sifh0oX7683n33Xb/loKjxWywWn8QRERHhVR78+HWsWOR/Jrp27aqQkBB/h3NeRd3+7rZfactBcSEPduShdObAzP6DX08FPXHihJo1ayabzeZ2+cSJE/W///1P06dP17fffqsKFSooMTFRp0+fdvS54447tHnzZi1fvlyLFy/WV199pXvvvbekVsE/0hZIm3o7fxmU7M/dFZkke7u7/u6++Er2dnf9D34k5y/Wsj/fNcn+hbwwtj5k7+9unMwUc+2e1rewRTXJQ/FJf4/tZs6DH7nPjbvikyMWN+N4itFTPJ76e4rT0zhuC1byvM09rVehi2oFzOlxDA/93RbVJLdFNcf4Jrahp/e3p/dfcY6Te9zNenn4rHn8mbDX3p62QIXizWfTUw48/Ww5+JG0sUfR4i/oZ+CuSYUbx1c5AwAAAHBB82thrUuXLnrqqad06623uiwzDENTpkzRI488ou7du6tp06Z66623tG/fPi1atEiStGXLFi1ZskSvv/66WrdurbZt2+qll17Se++9p3379pXw2pQQI9d+hIXHAoMf7ZpsP1WsIHnZfx8NA8BrZ3/WCvyZ8Hfb1pH2fgUpyc/mwY/sp4lK5uP36mfgOeP4KmcAAAAALngBe/OCnTt3Ki0tTR07dnS0RUVFqXXr1lq3bp0kad26dapcubJatmzp6NOxY0cFBQXp22+/9Th2VlaWMjMznR6lxpGvXY+wCBh/Xw+qIH9Mk+tRLwDMOeuzdt6fCYZ0eo+9X0FK+rO5dYz9X7Pxe/0z8KxxfJUzAAAAABe8gC2spaWlSZJiYmKc2mNiYhzL0tLSVKNGDafl5cqVU3R0tKOPOxMmTFBUVJTjUadOHR9HX4yy9vs7goKd9HBqZmGXAyic/M9SYX8mnK9fSX82T2y3/2s2/qL+DMza77ucAQAAALjgBWxhrTiNHTtWGRkZjseePXv8HVLhhdX0dwQFK9+gaMsBFE7+Z6mwPxPO16+kP5sV/r4Jjdn4i/ozMKym73IGAAAA4IIXsIW12NhYSVJ6uvOF09PT0x3LYmNjdeDAAaflZ86c0ZEjRxx93AkLC1NkZKTTo9SIbme/a518c/ci3wqW6g4tuEvdofZ+AIrgrM/aeX8mWKTwOvZ+BSnpz2ajSfZ/zcbv9c/As8bxVc4AAAAAXPACtrBWv359xcbGauXKlY62zMxMffvtt0pISJAkJSQk6OjRo9q4caOjz6pVq5SXl6fWrVuXeMwlwhIsNZ6a/8SvobiolywFhRbcJyjU3g+A987+rBX4M+Hv542m2PsVpCQ/m9W7S8ER9v+bjd+rn4HnjOOrnAEAAAC44Pm1sHb8+HFt2rRJmzZtkmS/YcGmTZu0e/duWSwWjRw5Uk899ZQ+/vhj/fzzzxo4cKBq1aqlHj16SJIaN26sm266Sffcc4++++47rVmzRsOGDVO/fv1Uq1Yt/61YcYvtKTWfL4Vd5NweXkeK8HA6V0SDv4/QOKd/ZLz7/pHx7vtX7y7Xo1qCpXpjpEYTCxd/o4n2/u7GiYw31+5pfctFFS4WSbJ4KAZGNHA/Z/Xu7nMTGiO3ykW5H8dTjJ7i8dTfU5yexlE5982etrmn9Qoq72F8E3N6HMNDf4+FFA8/yoLKy9Q29PT+9vT+K85xgiu5WS8PnzWPPxNq29tje6pQvPlsesqBp58t1btLLRYVLf6CfgbWG+MmHjfj+CpnAAKWzWZTXFyc4uM9/DwCAADwAU/fXkvEhg0b1KFDB8fz5GT70RJJSUmaPXu2HnroIZ04cUL33nuvjh49qrZt22rJkiUKDw////buPS6qOv8f+GsYmGEAgRTkoiimroh5F1m8pKblrdK0IjNDrfZbYsl6S2vTdvu5tlmttrm2taltpXZZL+V6ybxlZt4xTcVLWl5ASQNEkcvM5/fHYUYG5sBcznCYw+v5ePDA+cxnPuf9+ZyLZ96c8zm2z3z88ceYOHEi+vfvDz8/P4wcORJvvfVWrfel1kWPAKKGlT/dLluaC6hhb+kKi7JC4NAYaTLyoJZAxw8B/xBAmB3XNxdJT+i7flKa9yhhnnQ1iVx9S4n0BEFr+80n1HylWmUJrwG/+3+O25FrX65cLn5H42ApAXb3AYovAsZYIHk7YGgo34bcMuXGpjQf2DcUuPkLENgM6PY/ICBMvh25+nLr0NU4i3OBXd2BklzAEAmk7AGMka6vc7k4HbVjKXFc15U29Cb52OXaKbnqeN26ug5d3f682Y4wO7+vVXdM8Pa+6eqxRYn4q6vfZq5z7Sg1ZkRUJ6WnpyM9PR0FBQUIC3PhD25ERERELtAJIYTaQajNesKVn5/vW/OtERERkWp4/uAbvL2eSktLsW7dOowaNQpFRUWKt18bPP06EBQUhOXLl6s2Bp7Gr9MpM72KyWRyaxy09nXMuk8MGTIEAQEBaodTI0/Xv6P152tj4C0cBwnHwTfHwJXzhzo7xxoREREREREREVFdxsQaERERERERERGRG5hYIyIiIiIiIiIicgMTa0RERERERERERG5gYo2IiIiIiIiIiMgNTKwRERERERERERG5gYk1IiIiIiIiIiIiNzCxRkRERERERERE5AYm1oiIiIiIiIiIiNzAxBoREREREREREZEbmFgjIiIiIs1ZuHAhEhMTkZSUpHYoRERE5IBOp/Pop67wVzsA8oAwA1d3AMXZgDEGaNgb0Only11tR6341Wqnttv2JUpta6QMtfYdX9hniYjKpaenIz09HQUFBQgLC1M7HCIiItIoJtZ8Vc5K4NgkoPj8rTJjUyBmFJC9vGp52wVA9Ajn25Gr7+34XV2uN+NXa2zqGqW2NVKGWvuOL+yzREREREREtYy3gvqinJVA5oP2X0wB6fXZeQ7KL0j1c1Y62Y5MfaUotVxvxq/W2NQ1Sm1rpAy19h1f2GeJiIiIiIhUwMSarxFm6WoPCFc+JP06niF9vsZ2HNRXilLL9Wb8ao1NXaPUtkbKUGvf8YV9loiIiIiISCVMrPmaqzuqXu3hFAHcPCd93ql2KtVXilLL9Wb8ao2NM4QZuLINuLhc+u3NJIRS2xopQ619xxf2WSIiIiIiIpVwjjVfU5ytzOedbcfT5bnbXk31vBm/WmNTk9qem0qpbY2Uoda+4wv7LBERERERkUp4xZqvMcYo83ln2/F0ee62V1M9b8av1thUR425qZTa1kgZau07vrDPEhERERERqYSJNV9zWw8Aejc+qAMC44CGvaWXDXtLVztB51x9pSi1XG/Gr9bYyFFrbqoax0FOLY9PfaHWvuML+ywREREREZFKmFjzNb99B8DVBEr5F9mE+YCuPCmn00u3EFZ8v7r6SlFqud6MX62xkaPW3FTVjoPsh6RftTk+9YVa+44v7LNEREREREQqYWLN17gz/1BgU6DT51Xn4YoeIZUbmzhXXylKLdeb8as1No6oOTeV7DjEAfHTyq9AqliuwvjUJ2rtO76wzxIREREREamADy/wNc7OP9Tm74AxSqrfsLf8VSDRI4CoYeVXRWXXXF8pSi3Xm/GrNTaVqT03VXXj0Gau+uNT36i17/jCPktERERERFTLmFjzNmH2/AukXRuNAUMToOQiHM+5pZOu/oh/tupyzEXA8WnA9ZNAcGsgYR6gN0nlZxcAN04DQS2BsG6Af4h87HLtWEqAn/95q53mEwA/g3y5nOJcYFd3oCQXMEQCKXsAY6RrYwY4Xi7gOJbq+pTz+a3y8ORbY+aoflkhcGjMrfY7fiiNpVx9uTGuGLspvuZ1bmwCwAxcXG7fjqvxyNUXZqAg81b5bT1ubQuVtx1hBvYNBW7+AgQ2A7r9DwgIcz0Wue1Grp3SfMfLdXW7lCuvbv9xpVxunTvql5/BcSw6PdCob9VNQa5tJY5DgPxyldr35eq7Gr8r9ZUaGyIiIiIiqpd0QghH39TrlYKCAoSFhSE/Px+hoaHKNZyzUpp0vuL8WMam0jxDzt7y5KiNgEZA6RVI8xJVXH3l8xQ5uqVq/3Agd03V9v3DgLL8quWmloCluGrsxhigYK/j+kVnYT//mx4I7QIUHKhaHnkvULC/avulVwHLDcdxDsirWi43xqFdgdy1lZZrndep4pjppcRV0emqbRuigJJLzpfLjaVceWiS9GXeqdj9AFiqtmFl2yYqtONndNwvV+M0RAElv6LKOjREOB4HR3QGQJQ4v0y57UlumdW2X1i1HbntUq5cqW1Ebp3Lrasq9ED8ZCDhtapvye0LMaOA7OXOl7tyfAKA49OBs2/C433f4XZfTX25OF057ipxjKZ6zWvnD6Qob6+n0tJSrFu3DqNGjUJRUZHi7dcGT78OBAUFYfny5aqNgafx63SuPqjJMZPJ5NY4aO3rmHWfGDJkCAICAtQOp0aern9H68/XxsBbOA4SjoP8GHhj/1OKK+cPvGLNW3JWApkPosoVRsUXpHJn5hOSa6P0qvTbvyFQViGZEthUmvzb2aQa4DipATj+kl98Xn4SfYdJAbPjJBzMjuOpboL+snzg63D75JrsGJ8Hch215WinM8snNOSSRnLlcmMpV+5obGRjryapBtgn1aztyHE1Tof9NTufVAMcJ72qW6bc9iS3TJfar2a7lCtXahuRW+dOMwNn50n/rJhcq25fsNZ3qtyF4xNQnlRz0I47+77D7V6uvkycrhx3lThGExERERFRvceHF3iDMEtXQThM5JSXHc+Q6rndhg7wNwHdvgY6LAOStgJ9zlT9Imgukk+q+ZqyfOk2UaCG8SHSuLNvSrdNAgrvC04enwBp+WffVGCZ7nAQpyvHXSWO0URERERERGBizTuu7qjhKhQB3Dwn1fOojfPSXECxo6R5jxzNC3R8mpNB+4hd3aXfNY4PkZaZpbnIAC/sC04cn4Dy5auZeKoUpyvHXSWO0UREREREROCtoN5RnO15PSXaAKSJ07WkpPyKNWfHh0irbpTfnuqtfaGmdm/I3B5b26xxKnXMdLcuERERERHVS7xizRuMMZ7XU6INQHoaoZYYyp8O6uz4EGlVUEvpt7f2hZratS5fbdY4XTlmKnV8JSIiIiKieo+JNW9o2Ft6shzknnChAwLjpHrebAMAEhxNLO7DUvZIv2scHyIt0wPNJ0j/VHxfcPLY0nyCFIdqKsXpyjFTqeMrEdVpCxcuRGJiIpKSktQOhcirdDqdRz9EROQZJta8QacH2i6wvqj8pvQrYb7jOdGUbAMA9CYgclhNEfsG/zDAWH7FWrXjQ6Rx8ZMBP4P0b0X3BReOLX4GKQ5VOIjTlWOmUsdXIqrT0tPTcfToUezd6+gpxURERETKYGLNW6JHAJ0+B4xN7MsDm0rllZ/e6a02AKDravnkmn+Y43JTy/IrOipS+ktmpfYC4wC/IMdV/cOAAXn2ZXLj41KceqmvjhiiXCuXG8uall9RYFz5unJ1rB20I9cvuTjdit9JOoN3l1lt+5XHUg+EJrlWrtQ2EppUdb+qbl1VoQfipwEJr9kXyx4r4qT6jpbpsNzFY0vCa1I7roxl5DDH8Tjc7uXqy8TpyjFTqeMrERERERHVa3x4gTdFjwCihpU/gS5bmq+nYW/XroJQog1ASq6Zi6SnhF4/Kc29ljBPuqKtrBA4NEaajDyoJdDxQ8A/BBDmW8stvgRk/bHm5cSlQ7riQwDnFtZQ2Qy0+TtgjLLvV3Gu9PTPklxpTrWUPbeuVKtpfJyNs/Fw4LY+0u1sfgb5sXG1vOJY+ocC+TvdGwNLifTUxRunPRtLuXWrWPwAwnoCZQW32hdmYN9Q4OYvQGAzoNv/gIAw52LRBwG5q11fpn8IUJrveLkVxzKo5a117mq5UttIxf2qpnXlZ3AciyPVHSvazHWt3BUJrwG/+3+ujaXcGLha39Vx8KQuERERERGRA0yseZtODzTq61kblhIg5/NbX9DDk6tPiMmV6/RAUCsAOqnc+uVRbwLiJ936Yqk3SeVlhcCJP0mJCj+Tc7FmfwJYipyvn/s/AH72/fIzSFeRCLP025pIkEtUVBwfCOeWe/30rWUAUjLvwgeA5QaQ9x3QYjoQ1Exa5tXtQPFF6cdcdGvsc/8nJf9unABazb4Ve8M+0lUvZXnOJaZ++SdguWmfDBJm4Map8j5Z3B/LskIgb5cUS8ll6bU1aXrzgrTO9UG3xqEiy00nxzJLWufma7fGxxApJb4Mkbe2M0uJ1KfiiwCE9BqwH0tnt5uin6V1VbmdwqNSHGWF0k9AmLT80E63ko7WeMxF0nZjTcQ1HXcrGXR1u7T/3DwPxD15q7wgU6pvvi691jsZb0UV96vK67wkt3xd5Uqv7bYDcWs9VZcUdHSsEGYpdmv923rcuiXS0fHJmWR7xSSU3BjLxS+nunYcxV9dO54ed4mIiIiIiJygE0I4mYnQroKCAoSFhSE/Px+hoaFqh2Nv/3Agd03Vcv8woCy/arlfUHnCoRJDFFDyK4CKX2z1QOS9QMF+oPj8rWJjU0CUAiWXPAzeDXLxV9svFeJ0RFd+ZQ1qSB7URK6vrjcEx0m58isKvbZcR/wBlHmp7RqWa4x2fvvWGQBR4ny5q9ul7Bi7MD6mlkDRWVTZl03xQNFp5+vHT656SykAfNcdKHAwH5GpJWAprjqWMaOA7OVVy40xjtsJTSq/utTJdkK7ArlrnY/fFTkrgWOTqi6z7QLeCkpOqdPnD2Tj7fVUWlqKdevWYdSoUSgqKlK8/drg6deBoKAgLF++XLUx8DR+pSbwN5lMbo2D2vEr/XXQuk8MGTIEAQEBirbtDd4YP18bA2/hOEg4DvJjUNeOXxW5cv7AK9bqMrmkGuA4qQbIJ0YcJp/Mjtuv+CWztsnF71K/VOIo8eIOxZJbcle6yRx8vJZUA9RJqpUvt/L2XN32LbcO5cpd3S5lx9iF8XGUPINZprya+mfLnxhcMTkll1STa6f4/K12KpfLjbOj9qtrJ9dROzLxuyJnJZD5IKrsD8UXpHLOs0ZERERERE7gwwvqKnORfFKNiEgJZ9+0v5VWLqlWV1WM3xXCLF2p5jDJXF52PKPmW1eJiIiIiKjeY2Ktrjo+Te0IiEjzzNJcbYA0p5rPqRC/K67uqOHKXAHcPCfVIyIiIiIiqgZvBa1rrJOSX/5C7UiIqD44/z6Qux4o2Kd2JO7J+dz1p4gWZzvXtrP1quPKE02JiIiIiMjnMLFWlxyfLt3a5Onk90REzrp+RPrxVfk7bz19N2uq/ANZKj6QwBjjXNvO1pPDhyMQEREREWkebwWtK45PL5+Mm0k1IiL3lD+QpcoDK8ofSJCzUnrdsLeU4ILcU4h0QGCcVM9d1ocj1BQLERERERH5NCbW6gJLSfmVakREpLxKDyTQ6aWrxgBUTa6Vv06Y7/4tm3w4AhERERFRvcHEWl3w8z/BK9WIiCoxNgEih0H+yjJXlD+Q4MCDwJ6BwJUtQIePpWVUFNgU6PS5/K2awgxc2QZcXC79dpQc48MRiIiIiIjqDc6xVhfcOK12BEREdU9xNlB8EVJizdHVX27IXS39vvoVcG4hEHE/0OFD5x4u4OycabX5cAQiIiIiIlIVE2tqqfikOKW+MBIRaYoFKNjr3UX8+gWg0wFdV1dfzzpnWuXjtXXOtIpXudXWwxGIiIiIiEh1TKypwdFVD0REpI7cNYC5CNCbHL9f45xpOmnOtKhh0tVu1ocjFF+Q+YxOuuXUk4cjEBERERFRncA51mqb3JPiiIhIPfvvV27ONLuHI8jw5OEIRERERERUZzCxVpuqveqBiIhUc/VrYG8/YGtz6Q8gFbkzZ1r0CCB+KoDKyTO9VC73cAQiIiIiIvIpTKzVphqveiAiquMCooC4iUBYT7Uj8Y6SC0DmSPvkmjtzpuWsBM6+jqpPfLZI5ZWTd0RERERE5JM4x1pt4hPgiMjXlV4Czr2tdhTed/hJoOgMcOMsYIoHDE2Akotwas40V+dk80TFB+HU9FRTIiIiIiJSHBNrtcnYWO0IiIjIGebfgKypFQr8YEuK2SXMdNKvinOmuTInW6O+7seYsxI4+px0lZ2VoQmQ+BZvNSUiIiIiqiW8FbQ2OZoUm4iIfIBF+qUPti8ObAJ0+tw+keXOnGyuylkp3bJaMakGOL6VlYiIiIiIvIZXrHmbpQT4+Z/AjdNA4TG1oyEiIk+YC+1fWxzc7unOnGw1KSsEDo2R/i8xtQB+21F9/R//oMytpkREREREVC0m1rzp+HTg7BuwXelARETaUnIByHwQ6PgJYIiUrkIzNAaMTau/HTSwqXQV88XlNc+N9l13oGDvrdfXD9ccV+kV4Mx8IDCWc68REREREXkRE2vecnw6cHae2lEQEZHXCeDQI7D7I4ouWLY2AKDkKrBvwK3XATFAu7eByMHA8WnA9ZNAcGsg73vg2n73wjpRYY44YxOgrZfnXqt4hXZQS6D5BMDPIF/f2w9ecDUeIiIiIiI3MLHmDZaS8ivViIiofqh0ZbK4XkP1G/avS7OludEquvqV52FZFZfPvZa4GPjpL0BJrnSFXcoewBgJ3MwBdnYCyvIA/3CgZyYQGC0lAHf3AYovAsZYIHk7YGgImIvsE4DwB84tsF9m1h+B+GlAm7lVE2iX1gDHnpPisrIm/6KGeZ5wc/THLWs8Ca+5PHykvrVr12LKlCmwWCx4/vnn8eSTT6odEhEREREADSXWFi5ciHnz5iEnJwcdO3bEP/7xD3Tv3l2dYM6+Dd7+SUREdc7R8bf+ffM6sNXB06pLLwHbHMz/VnYV2NII0BkAUXKrvLoE4Nl5wLlF9nPT+TcCyq5UrWtN/gU0km5ltTI2BdoucP5qu+quGLeWM7nmU8rKyjB58mRs3boVYWFh6Nq1Kx544AE0atRI7dCIiIiItPFU0E8++QSTJ0/G7NmzceDAAXTs2BEDBw7E5cuX1Qno6jZ1lktERORtFZNqzqj8wAdHSbWKSiu9X1w+j50zTzq1lNQ8DcPZeVI98hl79uxBu3bt0KRJE4SEhGDw4MH46isFr+gkIiIi8oAmEmtvvvkmnnrqKYwbNw6JiYl45513EBQUhMWLF6sT0K9fqrNcIiIizSl/8urxDGletupsHVn9+67WI0V88803uO+++xAbGwudTofVq1dXqbNw4ULEx8cjMDAQycnJ2LNnj+29ixcvokmTJrbXTZo0wYULF6q0QURERKQGn78VtKSkBPv378fMmTNtZX5+fhgwYAB27drl8DPFxcUoLi62vS4oKPB6nEREROQuAdw8J8291qivfLXStc4152w9UsT169fRsWNHjB8/HiNGVL2l13rnwTvvvIPk5GTMnz8fAwcORFZWFho3dnC7cg3kzvNKS0tRWlrqfkdkWNs0mUyKt11bPB0Xa9/VGgOl4veUu+OgdvxK7xfW9ryxv3mDN8bP18bAWzgOEo6D/BjUteOXu23rhBDCa5HUAutfMb/77jukpKTYyqdPn47t27dj9+7dVT7z8ssv489//nOV8vz8fISGhnoe1Aad520QERGRvQ7LgNhR8u+78v/vIM9PfwoKChAWFqbc+UM9oNPpsGrVKgwfPtxWlpycjKSkJLz99tsAAIvFgri4ODz77LOYMWMGvvvuO8ybNw+rVq0CAGRkZKB79+549NFHHS5D7jxv2bJlCAoKUr5TREREpDk3btzAo48+6tR5ns9fseaOmTNnYvLkybbXBQUFiIuLUzEiIiIiqpHRwUMVyKc5c+dB9+7dceTIEVy4cAFhYWFYv349XnrpJdk25c7z7rnnHq8kQEtLS7Fp0yaMHz8eRUVFirdfG/Lz8z36fHR0NBYvXqzaGHgaf1hYmCJxmEwmt8ZB7fiVXr6r4+Dp8j3ljfGzHhfuvvtuBAQEeNS+L+M4SDgO8mOg9vGrOq7c2ejzibWIiAjo9XpcunTJrvzSpUuIjo52+Bmj0Qij0ei9oExPAkX/9l77RERE9YoOCGwKNOxdfbUWbwNnJtbcXIu3lQmLPPbrr7/CbDYjKirKrjwqKgrHjx8HAPj7++ONN95Av379YLFYMH369GqfCCp3nhcQEODVLzRFRUU+m1jzdFys/VZrDJSKXymujoPa8Xtr+c6Og9qJBm+On7ePO76C4yDhOFQdA7WPX0q17fMPLzAYDOjatSs2b95sK7NYLNi8ebPdraG1qs976iyXiIhIc8pv70yYD+j01Vdtk+5ck87Wozrj/vvvx4kTJ3Dq1Cn84Q9/UDscIiIiIhufT6wBwOTJk/Hee+/hgw8+wLFjx/DMM8/g+vXrGDdunHpBKTB3CxERUZ2jMzhf19QSMDa1LwuMA0KTHNcPTXJQvynQ6XMguuqk9w7V9P8v/3+uU9y584CIiIioLtFEYi01NRWvv/46Zs2ahU6dOiEzMxMbNmyocltBrRskpNtCKzI9KX9S78rJvqttsFxeXYvR18vrUiy+Xl6XYtFEeeX/8vwAxFYqiy2v27xSeXOpPPQ5++LQ56Ty26bal982VSpPXGZfnris+vKk7+zLk76Tytt+ZF/e9qPqy3udBGC9fD1Aej1IAP0uA4HxgF+w9LvfZam8bzYQEAXojNLvvtlS+V1XgOA7AP+G0u+7rgADi4G7bwBx6UDDe6Tfd98ABlwDIocDwe2l3wOuAX1OAX3PAklbpQcPJG0F+pwBeuxx3EaPPY7rO5tUsxokqt7u2eJtJtXqIG/eebBw4UIkJiYiKUkmkUtERESkAJ9/KqgS+FQvIiIichXPH5xTWFiIU6dOAQA6d+6MN998E/369UPDhg3RrFkzfPLJJ0hLS8O//vUvdO/eHfPnz8enn36K48ePK/JHUm+vp9LSUqxbtw6jRo3y2TnWPP06EBQUhOXLl6s2Bp7Gr9O58EThaphMJrfGQe34lV6+q+Og9tdRb4yf9bgwZMiQej2nFsdBwnGQHwO1j1/VceX8wecfXkBEREREdde+ffvQr18/22vrEzvT0tKwdOlSpKamIjc3F7NmzUJOTg46depUN+48ICIiInICE2tERERE5DV9+/at8S/KEydOxMSJTjzRlYiIiKiO0cQca0RERERERERERLWNiTUiIiIi0hw+vICIiIhqAxNrRERERKQ56enpOHr0KPbu3at2KERERKRhTKwRERERERERERG5gYk1IiIiIiIiIiIiNzCxRkRERERERERE5AYm1oiIiIiIiIiIiNzAxBoRERERaQ6fCkpERES1gYk1IiIiItIcPhWUiIiIagMTa0RERERERERERG5gYo2IiIiIiIiIiMgNTKwRERERERERERG5gYk1IiIiIiIiIiIiNzCxRkRERERERERE5AYm1oiIiIhIcxYuXIjExEQkJSWpHQoRERFpmL/aAdQFQggAQEFBgcqREBERka+wnjdYzyOobklPT0d6ejry8/MRHh7utfO80tJS3Lhxw6e3A0/HRgih6hjUlXN4d8dB7fiVXr6r46B2/z3lKH7rcaGgoAABAQEqRFU3cBwkHAfvjYE3jx+unOfphC+fBSjk/PnziIuLUzsMIiIi8kHnzp1D06ZN1Q6DZPA8j4iIiNzlzHkeE2sALBYLLl68iAYNGkCn06kdDgoKChAXF4dz584hNDRU7XC8in3VpvrUV6B+9Zd91a761F+l+iqEwLVr1xAbGws/P86uUVd5+zyvPu07cjgGEo6DhOPAMbDiOEg4Dr45Bq6c5/FWUAB+fn518i/NoaGhPrPReYp91ab61FegfvWXfdWu+tRfJfoaFhamUDTkLbV1nlef9h05HAMJx0HCceAYWHEcJBwH3xsDZ8/z+OdVIiIiIiIiIiIiNzCxRkRERERERERE5AYm1uogo9GI2bNnw2g0qh2K17Gv2lSf+grUr/6yr9pVn/pbn/pK3sftiWNgxXGQcBw4BlYcBwnHQftjwIcXEBERERERERERuYFXrBEREREREREREbmBiTUiIiIiIiIiIiI3MLFGRERERERERETkBibWiIiIiIiIiIiI3MDEmkrmzp2LpKQkNGjQAI0bN8bw4cORlZVlV+fmzZtIT09Ho0aNEBISgpEjR+LSpUsqRey+RYsWoUOHDggNDUVoaChSUlKwfv162/ta6acjr776KnQ6HTIyMmxlWurvyy+/DJ1OZ/eTkJBge19LfQWACxcu4LHHHkOjRo1gMpnQvn177Nu3z/a+EAKzZs1CTEwMTCYTBgwYgJMnT6oYsXvi4+OrrFedTof09HQA2luvZrMZL730Elq0aAGTyYSWLVvilVdeQcVn+2hl3QLAtWvXkJGRgebNm8NkMqFHjx7Yu3ev7X1f7es333yD++67D7GxsdDpdFi9erXd+8706+rVqxg9ejRCQ0MRHh6OJ554AoWFhbXYC/I1CxcuRHx8PAIDA5GcnIw9e/aoHZKiuF8pd87+yy+/YOjQoQgKCkLjxo0xbdo0lJWV1WZXPKLE+byvj0Fl7p7n+/o4KHH+7+tjACjzvcDXj49KfGfQwrYAABCkioEDB4olS5aII0eOiMzMTDFkyBDRrFkzUVhYaKvz9NNPi7i4OLF582axb98+8fvf/1706NFDxajd88UXX4j//e9/4sSJEyIrK0u88MILIiAgQBw5ckQIoZ1+VrZnzx4RHx8vOnToICZNmmQr11J/Z8+eLdq1ayeys7NtP7m5ubb3tdTXq1eviubNm4uxY8eK3bt3i59++kls3LhRnDp1ylbn1VdfFWFhYWL16tXi0KFD4v777xctWrQQRUVFKkbuusuXL9ut002bNgkAYuvWrUIIba1XIYSYM2eOaNSokVi7dq04c+aM+Oyzz0RISIhYsGCBrY5W1q0QQjz88MMiMTFRbN++XZw8eVLMnj1bhIaGivPnzwshfLev69atEy+++KJYuXKlACBWrVpl974z/Ro0aJDo2LGj+P7778WOHTtEq1atxKhRo2q5J+QrVqxYIQwGg1i8eLH48ccfxVNPPSXCw8PFpUuX1A5NMdyvlDlnLysrE3fccYcYMGCAOHjwoFi3bp2IiIgQM2fOVKNLbvH0fF4LY1CRu+f5WhgHT8//tTAGSn0v8PXjo6ffGbSwLVgxsVZHXL58WQAQ27dvF0IIkZeXJwICAsRnn31mq3Ps2DEBQOzatUutMBVz2223iX//+9+a7ee1a9dE69atxaZNm0SfPn1s/+Fqrb+zZ88WHTt2dPie1vr6/PPPi169esm+b7FYRHR0tJg3b56tLC8vTxiNRrF8+fLaCNFrJk2aJFq2bCksFovm1qsQQgwdOlSMHz/ermzEiBFi9OjRQghtrdsbN24IvV4v1q5da1fepUsX8eKLL2qmr5UTAM706+jRowKA2Lt3r63O+vXrhU6nExcuXKi12Ml3dO/eXaSnp9tem81mERsbK+bOnatiVN7D/Urizjn7unXrhJ+fn8jJybHVWbRokQgNDRXFxcW12wEFuXI+r6Ux8OQ8Xwvj4On5vxbGQInvBVo8Prr6nUEL24IVbwWtI/Lz8wEADRs2BADs378fpaWlGDBggK1OQkICmjVrhl27dqkSoxLMZjNWrFiB69evIyUlRbP9TE9Px9ChQ+36BWhzvZ48eRKxsbG4/fbbMXr0aPzyyy8AtNfXL774At26dcNDDz2Exo0bo3Pnznjvvfds7585cwY5OTl2/Q0LC0NycrJP9teqpKQEH330EcaPHw+dTqe59QoAPXr0wObNm3HixAkAwKFDh/Dtt99i8ODBALS1bsvKymA2mxEYGGhXbjKZ8O2332qqrxU5069du3YhPDwc3bp1s9UZMGAA/Pz8sHv37lqPmeq2kpIS7N+/326b8vPzw4ABA3x6X3FFfd2v3Dln37VrF9q3b4+oqChbnYEDB6KgoAA//vhjLUavDHfO57U0Bp6c52tlHDw5/9fCGCjxvUBrx0d3vjNoYVuw8lc7AAIsFgsyMjLQs2dP3HHHHQCAnJwcGAwGhIeH29WNiopCTk6OClF65vDhw0hJScHNmzcREhKCVatWITExEZmZmZrqJwCsWLECBw4csJuzyEpr6zU5ORlLly5FmzZtkJ2djT//+c/o3bs3jhw5orm+/vTTT1i0aBEmT56MF154AXv37sVzzz0Hg8GAtLQ0W58q/sdgfe2L/bVavXo18vLyMHbsWADa24YBYMaMGSgoKEBCQgL0ej3MZjPmzJmD0aNHA4Cm1m2DBg2QkpKCV155BW3btkVUVBSWL1+OXbt2oVWrVprqa0XO9CsnJweNGze2e9/f3x8NGzb06b6Td/z6668wm80Ot6njx4+rFFXtqo/7lbvn7Dk5OQ7Hyfqer/DkfF4rY+Dpeb4WxsHT838tjIES3wu0dnx05zuDFrYFKybW6oD09HQcOXIE3377rdqheE2bNm2QmZmJ/Px8fP7550hLS8P27dvVDktx586dw6RJk7Bp06YqV4RokfWKHgDo0KEDkpOT0bx5c3z66acwmUwqRqY8i8WCbt264a9//SsAoHPnzjhy5AjeeecdpKWlqRyd97z//vsYPHgwYmNj1Q7Faz799FN8/PHHWLZsGdq1a4fMzExkZGQgNjZWk+v2ww8/xPjx49GkSRPo9Xp06dIFo0aNwv79+9UOjYioTqsP5+zVqS/n83Lq23m+nPp0/i+nvn4vqE59+M5QHd4KqrKJEydi7dq12Lp1K5o2bWorj46ORklJCfLy8uzqX7p0CdHR0bUcpecMBgNatWqFrl27Yu7cuejYsSMWLFiguX7u378fly9fRpcuXeDv7w9/f39s374db731Fvz9/REVFaWp/lYWHh6O3/3udzh16pTm1m1MTAwSExPtytq2bWu79N3ap8pPuvHV/gLAzz//jK+//hpPPvmkrUxr6xUApk2bhhkzZuCRRx5B+/btMWbMGPzxj3/E3LlzAWhv3bZs2RLbt29HYWEhzp07hz179qC0tBS333675vpq5Uy/oqOjcfnyZbv3y8rKcPXqVZ/uO3lHREQE9Hq95vYVV9S3/cqTc/bo6GiH42R9z1d4cj6vhTFQ4jxfC+NQmavn/1oYAyW+F2jp+OjudwYtbAtWTKypRAiBiRMnYtWqVdiyZQtatGhh937Xrl0REBCAzZs328qysrLwyy+/ICUlpbbDVZzFYkFxcbHm+tm/f38cPnwYmZmZtp9u3bph9OjRtn9rqb+VFRYW4vTp04iJidHcuu3ZsyeysrLsyk6cOIHmzZsDAFq0aIHo6Gi7/hYUFGD37t0+2V8AWLJkCRo3boyhQ4fayrS2XgHgxo0b8POz/+9Qr9fDYrEA0Oa6BYDg4GDExMTgt99+w8aNGzFs2DDN9tWZfqWkpCAvL8/uyr0tW7bAYrEgOTm51mOmus1gMKBr165225TFYsHmzZt9el9xRX3Zr5Q4Z09JScHhw4ftvkRv2rQJoaGhVb6c+xJXzue1MAZKnOdrYRwqc/X8XwtjoMT3Ai0cH63c/c6ghW3BRu2nJ9RXzzzzjAgLCxPbtm2ze0TtjRs3bHWefvpp0axZM7Flyxaxb98+kZKSIlJSUlSM2j0zZswQ27dvF2fOnBE//PCDmDFjhtDpdOKrr74SQminn3IqPi1ICG31d8qUKWLbtm3izJkzYufOnWLAgAEiIiJCXL58WQihrb7u2bNH+Pv7izlz5oiTJ0+Kjz/+WAQFBYmPPvrIVufVV18V4eHhYs2aNeKHH34Qw4YNq/JYbV9hNptFs2bNxPPPP1/lPS2tVyGESEtLE02aNBFr164VZ86cEStXrhQRERFi+vTptjpaWrcbNmwQ69evFz/99JP46quvRMeOHUVycrIoKSkRQvhuX69duyYOHjwoDh48KACIN998Uxw8eFD8/PPPQgjn+jVo0CDRuXNnsXv3bvHtt9+K1q1b+9Rj76l2rVixQhiNRrF06VJx9OhR8Yc//EGEh4fbPd3M13G/UuacvaysTNxxxx3innvuEZmZmWLDhg0iMjJSzJw5U40uucXT83ktjIEjrp7na2EcPD3/18IYKPW9wNePj0J49p1BC9uCFRNrKgHg8GfJkiW2OkVFRWLChAnitttuE0FBQeKBBx4Q2dnZ6gXtpvHjx4vmzZsLg8EgIiMjRf/+/W3/CQuhnX7Kqfwfrpb6m5qaKmJiYoTBYBBNmjQRqamp4tSpU7b3tdRXIYT48ssvxR133CGMRqNISEgQ7777rt37FotFvPTSSyIqKkoYjUbRv39/kZWVpVK0ntm4caMA4DB+ra3XgoICMWnSJNGsWTMRGBgobr/9dvHiiy/aPeZbS+v2k08+EbfffrswGAwiOjpapKeni7y8PNv7vtrXrVu3Ovx/NS0tTQjhXL+uXLkiRo0aJUJCQkRoaKgYN26cuHbtmgq9IV/xj3/8QzRr1kwYDAbRvXt38f3336sdkqK4Xyl3zn727FkxePBgYTKZREREhJgyZYooLS2t5d64T4nzeV8fA0fcOc/39XFQ4vzf18dACGW+F/j68VEIz78zaGFbEEIInRBC1NbVcURERERERERERFrBOdaIiIiIiIiIiIjcwMQaERERERERERGRG5hYIyIiIiIiIiIicgMTa0RERERERERERG5gYo2IiIiIiIiIiMgNTKwRERERERERERG5gYk1IiIiIiIiIiIiNzCxRkRERERERERE5AYm1ohIc7Zt2wadToe8vDy1QyEiIiIijTt+/Dh+//vfIzAwEJ06dXL582fPnoVOp0NmZqbTnxk7diyGDx/u8rKISHlMrBFRnTN27FjodDrodDoEBAQgKioKd999NxYvXgyLxVLj53v06IHs7GyEhYXVQrSOFRUVoWHDhoiIiEBxcbFqcRARERFVVPE8q+LPqVOn1A7NZ82ePRvBwcHIysrC5s2ba2WZCxYswNKlS2tlWURUPSbWiKhOGjRoELKzs3H27FmsX78e/fr1w6RJk3DvvfeirKxM9nOlpaUwGAyIjo6GTqfzWnwlJSXVvv/f//4X7dq1Q0JCAlavXu21OIiIiIhcZT3PqvjTokWLKvVqOt8hyenTp9GrVy80b94cjRo1qpVlhoWFITw8vFaWRUTVY2KNiOoko9GI6OhoNGnSBF26dMELL7yANWvWYP369XZ/ndPpdFi0aBHuv/9+BAcHY86cOXa3ghYUFMBkMmH9+vV27a9atQoNGjTAjRs3AADnzp3Dww8/jPDwcDRs2BDDhg3D2bNnbfWtl9vPmTMHsbGxaNOmTbXxv//++3jsscfw2GOP4f3336/y/vHjx9GrVy8EBgYiMTERX3/9NXQ6nV0SrqaYiIiIiNxhPc+q+KPX69G3b19MnDgRGRkZiIiIwMCBAwEAR44cweDBgxESEoKoqCiMGTMGv/76q62969ev4/HHH0dISAhiYmLwxhtvoG/fvsjIyLDVqXyeAwDh4eF253XOno+9/vrriImJQaNGjZCeno7S0lJbneLiYjz//POIi4uD0WhEq1at8P7770MIgVatWuH111+3iyEzM7PaK/YsFgv+8pe/oGnTpjAajejUqRM2bNhg16/9+/fjL3/5C3Q6HV5++WXZdl577TW0atUKRqMRzZo1w5w5cxzWNZvNeOKJJ9CiRQuYTCa0adMGCxYssKtT+VbQvn374tlnn0VGRgZuu+02REVF4b333sP169cxbtw4NGjQAK1atbI7J/7tt98wevRoREZGwmQyoXXr1liyZInDmIhIHhNrROQz7rrrLnTs2BErV660K3/55ZfxwAMP4PDhwxg/frzde6Ghobj33nuxbNkyu/KPP/4Yw4cPR1BQEEpLSzFw4EA0aNAAO3bswM6dOxESEoJBgwbZ/aV28+bNyMrKwqZNm7B27VrZOE+fPo1du3bh4YcfxsMPP4wdO3bg559/tr1vNptty969ezfeffddvPjii3ZtOBsTERERkZI++OADGAwG7Ny5E++88w7y8vJw1113oXPnzti3bx82bNiAS5cu4eGHH7Z9Ztq0adi+fTvWrFmDr776Ctu2bcOBAwdcWq6z5z5bt27F6dOnsXXrVnzwwQdYunSpXXLu8ccfx/Lly/HWW2/h2LFj+Ne//oWQkBDodDqMHz++SuJoyZIluPPOO9GqVSuHcS1YsABvvPEGXn/9dfzwww8YOHAg7r//fpw8eRIAkJ2djXbt2mHKlCnIzs7G1KlTHbYzc+ZMvPrqq3jppZdw9OhRLFu2DFFRUQ7rWiwWNG3aFJ999hmOHj2KWbNm4YUXXsCnn35a7Rh+8MEHiIiIwJ49e/Dss8/imWeewUMPPYQePXrgwIEDuOeeezBmzBjbH5atsaxfvx7Hjh3DokWLEBERUe0yiMgBQURUx6SlpYlhw4Y5fC81NVW0bdvW9hqAyMjIsKuzdetWAUD89ttvQgghVq1aJUJCQsT169eFEELk5+eLwMBAsX79eiGEEB9++KFo06aNsFgstjaKi4uFyWQSGzdutMUUFRUliouLa4z/hRdeEMOHD7e9HjZsmJg9e7bt9fr164W/v7/Izs62lW3atEkAEKtWrXI6JiIiIiJXpaWlCb1eL4KDg20/Dz74oBBCiD59+ojOnTvb1X/llVfEPffcY1d27tw5AUBkZWWJa9euCYPBID799FPb+1euXBEmk0lMmjTJVlbxPMcqLCxMLFmyRAjh/PlY8+bNRVlZma3OQw89JFJTU4UQQmRlZQkAYtOmTQ77fuHCBaHX68Xu3buFEEKUlJSIiIgIsXTpUtnxio2NFXPmzLErS0pKEhMmTLC97tixo925XmUFBQXCaDSK9957z+H7Z86cEQDEwYMHZdtIT08XI0eOtL2ufL7cp08f0atXL9vrsrIyERwcLMaMGWMry87OFgDErl27hBBC3HfffWLcuHGyyyQi5/irl9IjInKdEKLK3GndunWr9jNDhgxBQEAAvvjiCzzyyCP473//i9DQUAwYMAAAcOjQIZw6dQoNGjSw+9zNmzdx+vRp2+v27dvDYDBUuyyz2YwPPvjA7nL9xx57DFOnTsWsWbPg5+eHrKwsxMXFITo62lane/fudu04GxMRERGRq/r164dFixbZXgcHB9v+3bVrV7u6hw4dwtatWxESElKlndOnT6OoqAglJSVITk62lTds2LDGaTMqc/bcp127dtDr9bbXMTExOHz4MADptk69Xo8+ffo4XEZsbCyGDh2KxYsXo3v37vjyyy9RXFyMhx56yGH9goICXLx4ET179rQr79mzJw4dOuR0344dO4bi4mL079/f6c8sXLgQixcvxi+//GIb45qeONqhQwfbv/V6PRo1aoT27dvbyqxXyF2+fBkA8Mwzz2DkyJG2q9mGDx+OHj16OB0jEUmYWCMin3Ls2LEqk+tWPBl0xGAw4MEHH8SyZcvwyCOPYNmyZUhNTYW/v3QILCwsRNeuXfHxxx9X+WxkZKTTywGAjRs34sKFC0hNTbUrN5vN2Lx5M+6+++4a23AlJiIiIiJXBQcHy976WPl8p7CwEPfddx/+9re/VakbExPj9NNEdTodhBB2ZRXnRnP23CcgIKBKu9anxptMphrjePLJJzFmzBj8/e9/x5IlS5CamoqgoCCn+uAuZ+KqaMWKFZg6dSreeOMNpKSkoEGDBpg3bx52795d7eccjU3FMusfp63jNXjwYPz8889Yt24dNm3ahP79+yM9Pb3KPHREVD3OsUZEPmPLli04fPgwRo4c6fJnR48ejQ0bNuDHH3/Eli1bMHr0aNt7Xbp0wcmTJ9G4cWO0atXK7icsLMyl5bz//vt45JFHkJmZaffzyCOP2B5i0KZNG5w7dw6XLl2yfW7v3r127SgZExEREZG7unTpgh9//BHx8fFVzkmCg4PRsmVLBAQE2CV9fvvtN5w4ccKuncjISGRnZ9tenzx50jbXl3U5np77tG/fHhaLBdu3b5etM2TIEAQHB2PRokXYsGFDlfl5KwoNDUVsbCx27txpV75z504kJiY6FRMAtG7dGiaTCZs3b3aq/s6dO9GjRw9MmDABnTt3RqtWrbx2x0JkZCTS0tLw0UcfYf78+Xj33Xe9shwiLWNijYjqpOLiYuTk5ODChQs4cOAA/vrXv2LYsGG499578fjjj7vc3p133ono6GiMHj0aLVq0sLtdYfTo0YiIiMCwYcOwY8cOnDlzBtu2bcNzzz2H8+fPO72M3NxcfPnll0hLS8Mdd9xh9/P4449j9erVuHr1Ku6++260bNkSaWlp+OGHH7Bz50786U9/AnDrL4lKxURERETkifT0dFy9ehWjRo3C3r17cfr0aWzcuBHjxo2D2WxGSEgInnjiCUybNg1btmzBkSNHMHbsWPj52X/VvOuuu/D222/j4MGD2LdvH55++mm7q6mUOPeJj49HWloaxo8fj9WrV9vaqDjpv16vx9ixYzFz5ky0bt0aKSkp1bY5bdo0/O1vf8Mnn3yCrKwszJgxA5mZmZg0aZLTYxgYGIjnn38e06dPx3/+8x+cPn0a33//vcMnxwNSIm7fvn3YuHEjTpw4gZdeeqnKH2GVMGvWLKxZswanTp3Cjz/+iLVr16Jt27aKL4dI65hYI6I6acOGDYiJiUF8fDwGDRqErVu34q233sKaNWvs5tVwlk6nw6hRo3Do0CG7q9UAICgoCN988w2aNWuGESNGoG3btnjiiSdw8+ZNhIaGOr2M//znPwgODnY4f0b//v1hMpnw0UcfQa/XY/Xq1SgsLERSUhKefPJJ21NBAwMDFY2JiIiIyBPWK7bMZjPuuecetG/fHhkZGQgPD7clz+bNm4fevXvjvvvuw4ABA9CrV68qc7W98cYbiIuLQ+/evfHoo49i6tSpdrdgKnXus2jRIjz44IOYMGECEhIS8NRTT+H69et2dZ544gmUlJRg3LhxNbb33HPPYfLkyZgyZQrat2+PDRs24IsvvkDr1q2djgmQnsA5ZcoUzJo1C23btkVqaqptrrPK/u///g8jRoxAamoqkpOTceXKFUyYMMGl5TnDYDBg5syZ6NChA+68807o9XqsWLFC8eUQaZ1OVL7RnYiIat3OnTvRq1cvnDp1Ci1btlQ7HCIiIiKP9O3bF506dcL8+fPVDqWKHTt2oH///jh37pxtQn8iInfx4QVERCpYtWoVQkJC0Lp1a5w6dQqTJk1Cz549mVQjIiIi8pLi4mLk5ubi5ZdfxkMPPcSkGhEpgreCEhGp4Nq1a0hPT0dCQgLGjh2LpKQkrFmzRu2wiIiIiDRr+fLlaN68OfLy8vDaa6+pHQ4RaQRvBSUiIiIiIiIiInIDr1gjIiIiIiIiIiJyAxNrREREREREREREbmBijYiIiIiIiIiIyA1MrBEREREREREREbmBiTUiIiIiIiIiIiI3MLFGRERERERERETkBibWiIiIiIiIiIiI3MDEGhERERERERERkRv+P1hWkvPX3KerAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 1500x700 with 2 Axes>"
       ]
@@ -186,8 +187,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_data = df_train[\"DrivAge\"].values.reshape(-1, 1)\n",
-    "test_data = np.sort(df_test[\"DrivAge\"].values).reshape(-1, 1)"
+    "train_data = df_train[\"DrivAge\"].values.reshape(-1, 1).astype(np.float64)\n",
+    "test_data = np.sort(df_test[\"DrivAge\"].values).reshape(-1, 1).astype(np.float64)"
    ]
   },
   {
@@ -295,7 +296,411 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PoissonRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PoissonRegressor</label><div class=\"sk-toggleable__content\"><pre>PoissonRegressor()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PoissonRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PoissonRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PoissonRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "PoissonRegressor()"
@@ -516,7 +921,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Key generation time: 0.0002 seconds\n"
+      "Key generation time: 0.0027 seconds\n"
      ]
     }
    ],
@@ -545,7 +950,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Execution time: 0.0022 seconds per sample\n"
+      "Execution time: 0.0027 seconds per sample\n"
      ]
     }
    ],
@@ -742,6 +1147,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "sklearn_sparse_arg = (\n",
+    "    {\"sparse\": False} if \"1.1.\" in sklearn.__version__ else {\"sparse_output\": False}\n",
+    ")\n",
+    "\n",
     "log_scale_transformer = make_pipeline(FunctionTransformer(np.log, validate=False), StandardScaler())\n",
     "\n",
     "linear_model_preprocessor = ColumnTransformer(\n",
@@ -751,7 +1160,7 @@
     "        (\"log_scaled_numeric\", log_scale_transformer, [\"Density\"]),\n",
     "        (\n",
     "            \"onehot_categorical\",\n",
-    "            OneHotEncoder(sparse=False),\n",
+    "            OneHotEncoder(**sklearn_sparse_arg),\n",
     "            [\"VehBrand\", \"VehPower\", \"VehGas\", \"Region\", \"Area\"],\n",
     "        ),\n",
     "    ],\n",
@@ -1075,7 +1484,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Key generation time: 0.0002 seconds\n"
+      "Key generation time: 0.0007 seconds\n"
      ]
     }
    ],
@@ -1103,7 +1512,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "FHE execution time: 0.0181 seconds per sample\n",
+      "FHE execution time: 0.0392 seconds per sample\n",
       "\n",
       "scikit-learn (clear) deviance score: 1.5934\n",
       "Concrete ML (FHE) deviance score: 1.5925\n",
diff --git a/docs/getting-started/pip_installing.md b/docs/getting-started/pip_installing.md
index 433be6f9d..5425e9bd5 100644
--- a/docs/getting-started/pip_installing.md
+++ b/docs/getting-started/pip_installing.md
@@ -24,7 +24,7 @@ Depending on your OS/HW, Concrete ML may be installed with Docker or with pip:
 
 ### Python support
 
-- **Version**: In the current release, Concrete ML supports only `3.8`, `3.9`, `3.10` and `3.11` versions of `python`.
+- **Version**: In the current release, Concrete ML supports only `3.8`, `3.9`, `3.10`, `3.11` and `3.12` versions of `python`.
 - **Linux requirement**: The Concrete ML Python package requires `glibc >= 2.28`. On Linux, you can check your `glibc` version by running `ldd --version`.
 - **Kaggle installation**: Concrete ML can be installed on Kaggle ([see question on community for more details](https://community.zama.ai/t/how-do-we-use-concrete-ml-on-kaggle/332)) and on Google Colab.
 
diff --git a/flake8_others.cfg b/flake8_others.cfg
index f268c51d5..588624aab 100644
--- a/flake8_others.cfg
+++ b/flake8_others.cfg
@@ -4,4 +4,4 @@ max-line-length = 100
 per-file-ignores =
     __init__.py:F401,
     tests/sklearn/test_dump_onnx.py: E501
-extend-ignore = DAR,E203
+extend-ignore = DAR,E203,E226
diff --git a/flake8_src.cfg b/flake8_src.cfg
index 3a2e41610..0d8227caa 100644
--- a/flake8_src.cfg
+++ b/flake8_src.cfg
@@ -1,4 +1,4 @@
 [flake8]
 max-line-length = 100
 per-file-ignores = __init__.py:F401
-extend-ignore = E203
+extend-ignore = E203, E226
diff --git a/poetry.lock b/poetry.lock
index 86cbeb4cb..9b7f52a26 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,14 +1,14 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "accelerate"
-version = "1.0.0"
+version = "1.0.1"
 description = "Accelerate"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "accelerate-1.0.0-py3-none-any.whl", hash = "sha256:6fc65cd7a43c41530e3cf8ffb1fbe3ac6e416ec3f4c9a55ddf407d374b887b68"},
-    {file = "accelerate-1.0.0.tar.gz", hash = "sha256:e39c99327d37c4583b4e6b70946915072b56f892836942eca22e1d25b71d6d4b"},
+    {file = "accelerate-1.0.1-py3-none-any.whl", hash = "sha256:c6aa0c7b8a797cb150471e90e3ca36ac41f5d4b40512cdd6f058b8bf25589467"},
+    {file = "accelerate-1.0.1.tar.gz", hash = "sha256:e8f95fc2db14915dc0a9182edfcf3068e5ddb2fa310b583717ad44e5c442399c"},
 ]
 
 [package.dependencies]
@@ -33,119 +33,119 @@ testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized",
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.3"
+version = "2.4.4"
 description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
-    {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
+    {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"},
+    {file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"},
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.10.9"
+version = "3.10.11"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
 aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
-async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
+async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
@@ -181,13 +181,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.5.0"
+version = "4.5.2"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -198,7 +198,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -290,40 +290,32 @@ test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock
 
 [[package]]
 name = "astroid"
-version = "2.15.8"
+version = "3.2.4"
 description = "An abstract syntax tree for Python with inference support."
 optional = false
-python-versions = ">=3.7.2"
+python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
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 typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
-wrapt = [
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-    {version = ">=1.14,<2", markers = "python_version >= \"3.11\""},
-]
 
 [[package]]
 name = "asttokens"
-version = "2.4.1"
+version = "3.0.0"
 description = "Annotate AST trees with source code positions"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
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 ]
 
-[package.dependencies]
-six = ">=1.12.0"
-
 [package.extras]
-astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
-test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+astroid = ["astroid (>=2,<4)"]
+test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "async-lru"
@@ -341,13 +333,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "async-timeout"
-version = "4.0.3"
+version = "5.0.1"
 description = "Timeout context manager for asyncio programs"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -371,17 +363,17 @@ tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "autopep8"
-version = "2.0.4"
+version = "2.3.1"
 description = "A tool that automatically formats Python code to conform to the PEP 8 style guide"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-pycodestyle = ">=2.10.0"
+pycodestyle = ">=2.12.0"
 tomli = {version = "*", markers = "python_version < \"3.11\""}
 
 [[package]]
@@ -558,13 +550,13 @@ vision = ["torchvision", "tqdm"]
 
 [[package]]
 name = "cachecontrol"
-version = "0.14.0"
+version = "0.14.1"
 description = "httplib2 caching for requests"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -573,7 +565,7 @@ msgpack = ">=0.5.2,<2.0.0"
 requests = ">=2.16.0"
 
 [package.extras]
-dev = ["CacheControl[filecache,redis]", "black", "build", "cherrypy", "furo", "mypy", "pytest", "pytest-cov", "sphinx", "sphinx-copybutton", "tox", "types-redis", "types-requests"]
+dev = ["CacheControl[filecache,redis]", "build", "cherrypy", "codespell[tomli]", "furo", "mypy", "pytest", "pytest-cov", "ruff", "sphinx", "sphinx-copybutton", "tox", "types-redis", "types-requests"]
 filecache = ["filelock (>=3.8.0)"]
 redis = ["redis (>=2.10.5)"]
 
@@ -883,23 +875,26 @@ files = [
 
 [[package]]
 name = "concrete-python"
-version = "2.8.1"
+version = "2.9.0rc1.dev20241130"
 description = "A state-of-the-art homomorphic encryption framework"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -982,7 +977,10 @@ files = [
 ]
 
 [package.dependencies]
-numpy = {version = ">=1.16,<2.0", markers = "python_version <= \"3.11\""}
+numpy = [
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+    {version = ">=1.26.0rc1,<2.0", markers = "python_version >= \"3.12\""},
+]
 
 [package.extras]
 bokeh = ["bokeh", "selenium"]
@@ -1080,38 +1078,38 @@ toml = ["tomli"]
 
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 python-versions = ">=3.7"
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 [package.dependencies]
@@ -1124,7 +1122,7 @@ nox = ["nox"]
 pep8test = ["check-sdist", "click", "mypy", "ruff"]
 sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["certifi", "cryptography-vectors (==43.0.1)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
+test = ["certifi", "cryptography-vectors (==43.0.3)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -1177,33 +1175,37 @@ files = [
 
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 [[package]]
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 [[package]]
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+version = "2.21.0"
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 [[package]]
 name = "flake8-bugbear"
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 ]
 
 [[package]]
 name = "fsspec"
-version = "2024.9.0"
+version = "2024.10.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1686,13 +1716,13 @@ lxml = ["lxml"]
 
 [[package]]
 name = "httpcore"
-version = "1.0.6"
+version = "1.0.7"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c"},
 ]
 
 [package.dependencies]
@@ -1707,13 +1737,13 @@ trio = ["trio (>=0.22.0,<1.0)"]
 
 [[package]]
 name = "httpx"
-version = "0.27.2"
+version = "0.28.0"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpx-0.27.2-py3-none-any.whl", hash = "sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0"},
-    {file = "httpx-0.27.2.tar.gz", hash = "sha256:f7c2be1d2f3c3c3160d441802406b206c2b76f5947b11115e6df10c6c65e66c2"},
+    {file = "httpx-0.28.0-py3-none-any.whl", hash = "sha256:dc0b419a0cfeb6e8b34e85167c0da2671206f5095f1baa9663d23bcfd6b535fc"},
+    {file = "httpx-0.28.0.tar.gz", hash = "sha256:0858d3bab51ba7e386637f22a61d8ccddaeec5f3fe4209da3a6168dbb91573e0"},
 ]
 
 [package.dependencies]
@@ -1721,7 +1751,6 @@ anyio = "*"
 certifi = "*"
 httpcore = "==1.*"
 idna = "*"
-sniffio = "*"
 
 [package.extras]
 brotli = ["brotli", "brotlicffi"]
@@ -1732,13 +1761,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.25.2"
+version = "0.26.3"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1751,16 +1780,16 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 hf-transfer = ["hf-transfer (>=0.1.4)"]
-inference = ["aiohttp", "minijinja (>=1.0)"]
-quality = ["mypy (==1.5.1)", "ruff (>=0.5.0)"]
+inference = ["aiohttp"]
+quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.5.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 tensorflow-testing = ["keras (<3.0)", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
 torch = ["safetensors[torch]", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
@@ -2096,22 +2125,22 @@ type = ["pytest-mypy"]
 
 [[package]]
 name = "jedi"
-version = "0.19.1"
+version = "0.19.2"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
-    {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
+    {file = "jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9"},
+    {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"},
 ]
 
 [package.dependencies]
-parso = ">=0.8.3,<0.9.0"
+parso = ">=0.8.4,<0.9.0"
 
 [package.extras]
 docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
-testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jeepney"
@@ -2158,30 +2187,35 @@ files = [
 
 [[package]]
 name = "json5"
-version = "0.9.25"
+version = "0.10.0"
 description = "A Python implementation of the JSON5 data format."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.8.0"
 files = [
-    {file = "json5-0.9.25-py3-none-any.whl", hash = "sha256:34ed7d834b1341a86987ed52f3f76cd8ee184394906b6e22a1e0deb9ab294e8f"},
-    {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"},
+    {file = "json5-0.10.0-py3-none-any.whl", hash = "sha256:19b23410220a7271e8377f81ba8aacba2fdd56947fbb137ee5977cbe1f5e8dfa"},
+    {file = "json5-0.10.0.tar.gz", hash = "sha256:e66941c8f0a02026943c52c2eb34ebeb2a6f819a0be05920a6f5243cd30fd559"},
 ]
 
+[package.extras]
+dev = ["build (==1.2.2.post1)", "coverage (==7.5.3)", "mypy (==1.13.0)", "pip (==24.3.1)", "pylint (==3.2.3)", "ruff (==0.7.3)", "twine (==5.1.1)", "uv (==0.5.1)"]
+
 [[package]]
 name = "jsonpickle"
-version = "3.3.0"
-description = "Python library for serializing arbitrary object graphs into JSON"
+version = "4.0.0"
+description = "jsonpickle encodes/decodes any Python object to/from JSON"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "jsonpickle-3.3.0-py3-none-any.whl", hash = "sha256:287c12143f35571ab00e224fa323aa4b090d5a7f086f5f494d7ee9c7eb1a380a"},
-    {file = "jsonpickle-3.3.0.tar.gz", hash = "sha256:ab467e601e5b1a1cd76f1819d014795165da071744ef30bf3786e9bc549de25a"},
+    {file = "jsonpickle-4.0.0-py3-none-any.whl", hash = "sha256:53730b9e094bc41f540bfdd25eaf6e6cf43811590e9e1477abcec44b866ddcd9"},
+    {file = "jsonpickle-4.0.0.tar.gz", hash = "sha256:fc670852b204d77601b08f8f9333149ac37ab6d3fe4e6ed3b578427291f63736"},
 ]
 
 [package.extras]
-docs = ["furo", "rst.linker (>=1.9)", "sphinx"]
-packaging = ["build", "twine"]
-testing = ["bson", "ecdsa", "feedparser", "gmpy2", "numpy", "pandas", "pymongo", "pytest (>=3.5,!=3.7.3)", "pytest-benchmark", "pytest-benchmark[histogram]", "pytest-checkdocs (>=1.2.3)", "pytest-cov", "pytest-enabler (>=1.0.1)", "pytest-ruff (>=0.2.1)", "scikit-learn", "scipy", "scipy (>=1.9.3)", "simplejson", "sqlalchemy", "ujson"]
+cov = ["pytest-cov"]
+dev = ["black", "pyupgrade"]
+docs = ["furo", "rst.linker (>=1.9)", "sphinx (>=3.5)"]
+packaging = ["build", "setuptools (>=61.2)", "setuptools-scm[toml] (>=6.0)", "twine"]
+testing = ["PyYAML", "atheris (>=2.3.0,<2.4.0)", "bson", "ecdsa", "feedparser", "gmpy2", "numpy", "pandas", "pymongo", "pytest (>=6.0,!=8.1.*)", "pytest-benchmark", "pytest-benchmark[histogram]", "pytest-checkdocs (>=1.2.3)", "pytest-enabler (>=1.0.1)", "pytest-ruff (>=0.2.1)", "scikit-learn", "scipy", "scipy (>=1.9.3)", "simplejson", "sqlalchemy", "ujson"]
 
 [[package]]
 name = "jsonpointer"
@@ -2423,13 +2457,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.2.5"
+version = "4.3.1"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2451,9 +2485,9 @@ tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.3.5)"]
-docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
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+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.6.9)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<8.1.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.4.1)", "ipython (==8.16.1)", "ipywidgets (==8.1.5)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.2.post3)", "matplotlib (==3.9.2)", "nbconvert (>=7.0.0)", "pandas (==2.2.3)", "scipy (==1.14.1)", "vega-datasets (==0.9.0)"]
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 upgrade-extension = ["copier (>=9,<10)", "jinja2-time (<0.3)", "pydantic (<3.0)", "pyyaml-include (<3.0)", "tomli-w (<2.0)"]
 
@@ -2527,13 +2561,13 @@ urllib3 = "*"
 
 [[package]]
 name = "keyring"
-version = "25.4.1"
+version = "25.5.0"
 description = "Store and access your passwords safely."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2678,52 +2712,6 @@ files = [
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 ]
 
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-
 [[package]]
 name = "lazydocs"
 version = "0.4.8"
@@ -2795,13 +2783,13 @@ tests = ["pytest"]
 
 [[package]]
 name = "mako"
-version = "1.3.5"
+version = "1.3.6"
 description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3383,38 +3371,43 @@ typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "mypy"
-version = "1.11.2"
+version = "1.13.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -3424,6 +3417,7 @@ typing-extensions = ">=4.6.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
+faster-cache = ["orjson"]
 install-types = ["pip"]
 mypyc = ["setuptools (>=50)"]
 reports = ["lxml"]
@@ -3614,51 +3608,59 @@ test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
 [[package]]
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 ]
 
 [[package]]
 name = "notebook"
-version = "7.2.2"
+version = "7.0.7"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
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+jupyterlab-server = ">=2.22.1,<3"
 notebook-shim = ">=0.2,<0.3"
 tornado = ">=6.2.0"
 
 [package.extras]
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 docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
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+test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.22.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"]
 
 [[package]]
 name = "notebook-shim"
@@ -3679,39 +3681,84 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 
 [[package]]
 name = "numpy"
-version = "1.24.2"
-description = "Fundamental package for array computing in Python"
+version = "1.23.5"
+description = "NumPy is the fundamental package for array computing with Python."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.extras]
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 [[package]]
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 [[package]]
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 [[package]]
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 description = "passive checker of Python programs"
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 platformdirs = ">=2.2.0"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
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 [[package]]
@@ -5123,17 +5202,17 @@ files = [
 
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 python-versions = ">=3.8"
 files = [
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 [[package]]
@@ -5355,105 +5434,105 @@ rpds-py = ">=0.7.0"
 
 [[package]]
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 description = "Alternative regular expression module, to replace re."
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 python-versions = ">=3.8"
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 [[package]]
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 [[package]]
 name = "rich"
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 [[package]]
 name = "rpds-py"
-version = "0.20.0"
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 description = "Python bindings to Rust's persistent data structures (rpds)"
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@@ -6040,19 +6223,23 @@ win32 = ["pywin32"]
 
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 name = "shellingham"
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 requests = "*"
 six = "*"
 
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 [[package]]
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 python-versions = ">=3.7"
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 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
-dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
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 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
@@ -6862,13 +7110,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformers"
-version = "4.45.2"
+version = "4.46.3"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -6886,13 +7134,13 @@ tqdm = ">=4.27"
 [package.extras]
 accelerate = ["accelerate (>=0.26.0)"]
 agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
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@@ -6926,7 +7174,7 @@ torch = ["accelerate (>=0.26.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
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-video = ["av (==9.2.0)", "decord (==0.6.0)"]
+video = ["av (==9.2.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
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 python-versions = ">=3.7"
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 [package.dependencies]
@@ -7005,13 +7253,13 @@ files = [
 
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 [package.dependencies]
@@ -7144,13 +7392,13 @@ test = ["websockets"]
 
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 [[package]]
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 version = "1.6.2"
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 [[package]]
 name = "yarl"
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 python-versions = ">=3.8"
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+    {file = "yarl-1.15.2-cp39-cp39-win32.whl", hash = "sha256:5b48388ded01f6f2429a8c55012bdbd1c2a0c3735b3e73e221649e524c34a58d"},
+    {file = "yarl-1.15.2-cp39-cp39-win_amd64.whl", hash = "sha256:81dadafb3aa124f86dc267a2168f71bbd2bfb163663661ab0038f6e4b8edb810"},
+    {file = "yarl-1.15.2-py3-none-any.whl", hash = "sha256:0d3105efab7c5c091609abacad33afff33bdff0035bece164c98bcf5a85ef90a"},
+    {file = "yarl-1.15.2.tar.gz", hash = "sha256:a39c36f4218a5bb668b4f06874d676d35a035ee668e6e7e3538835c703634b84"},
 ]
 
 [package.dependencies]
@@ -7418,5 +7593,5 @@ type = ["pytest-mypy"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.8.1,<3.12"
-content-hash = "9cfc1c5179451a7ba4944770ae4b9685fb8a01769be070c44a56ef947c21ee39"
+python-versions = ">=3.8.1,<3.13"
+content-hash = "7ef78c238cb4dd54339b4e273aceeb3d11555940e92da06c18e0a9685e31b36c"
diff --git a/pylintrc b/pylintrc
index 7d5c1de85..da1c75af2 100644
--- a/pylintrc
+++ b/pylintrc
@@ -90,6 +90,7 @@ disable=bad-inline-option,
       comparison-of-constants, # fixme
       import-outside-toplevel, # fixme
       consider-using-generator, # fixme
+      too-many-ancestors
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option
 # multiple time (only on the command line, not in the configuration file where
diff --git a/pyproject.toml b/pyproject.toml
index 33c9ebfc4..dce01e65e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -33,16 +33,16 @@ readme = "README.md"
 [tool.poetry.dependencies]
 # Investigate if it is better to fix specific versions or use lower and upper bounds
 # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/2665
-python = ">=3.8.1,<3.12"
+python = ">=3.8.1,<3.13"
 # Based on the following link, explicitly indicating the source makes poetry only look for that
 # source, so we should only use this for Concrete Python's non-public releases
 # https://python-poetry.org/docs/1.7/repositories#project-configuration
 # concrete-python = {version="==2.7.0", source = "zama-pypi-cpu"}
-concrete-python = {version="==2.8.1", source = "zama-pypi-cpu"}
 concrete-ml-extensions = [
     {version = "0.1.2", platform = "linux" }
 ]
-setuptools = "65.6.3"
+concrete-python = {version="==2.9.0rc1.dev20241130", source = "zama-pypi-cpu"}
+setuptools = "75.3.0"
 skops = {version = "0.5.0"}
 xgboost = "1.6.2"
 skorch = "0.11.0"
@@ -53,16 +53,28 @@ torch = [
 ]
 typing-extensions = "^4.5.0"
 brevitas = "0.10.2"
-onnx = "1.16.1"
+onnx = "1.17.0"
 onnxoptimizer = "0.3.13"
 # onnxruntime versions supported by onnx versions and opsets can be found here :
 # https://onnxruntime.ai/docs/reference/compatibility.html#onnx-opset-support
 onnxruntime = "1.18"
 hummingbird-ml = {version="0.4.11", extras = ["onnx"]}
-scikit-learn = "1.1.3"
-scipy = "1.10.1"
-numpy = "1.24.2"
-protobuf = "^4.25.2"
+scikit-learn = [
+        {version = "1.1.3", python = "<3.9" }, 
+        {version = "1.4.0", python = ">=3.9.0"}        
+        ]
+scipy = [
+        {version = "1.10.1", python = "3.8"},
+        {version = ">=1.10.1", python = ">3.8"}
+        ]
+numpy = [
+        {version = "1.23.5", python = "<3.9" }, 
+        {version = "1.26.4", python = ">=3.9.0"}        
+        ]
+protobuf = [
+    {version = "^5.28.3", python = ">3.11"},
+    {version = "3.20.3", python = "<=3.11"}
+]
 pandas = "2.0.3"
 
 # Zama sources
@@ -78,7 +90,7 @@ optional = true
 [tool.poetry.group.dev.dependencies]
 isort = "^5.10.1"
 black = "^24.3.0"
-pylint = "^2.17.0"
+pylint = "^3.0.0"
 pytest = "7.4.1"
 pytest-cov = "^4.1.0"
 pytest-xdist = "^3.3.1"
@@ -117,7 +129,8 @@ mistletoe = "^0.9.0"
 ruff = "^0.0.191"
 myst_parser = "0.19.1"
 flake8-bugbear = "23.2.13"
-flake8 = "6.0.0"
+flake8 = "^7.1.0"
+pycodestyle = "^2.11.1"
 ipython = "^8.10.0"
 markdown-it-py = "2.2.0"
 # torchvision's version depends on torch but is not enforced by the requirements of the
@@ -156,6 +169,7 @@ filterwarnings = [
     "ignore:open_text is deprecated.*:DeprecationWarning",
     "ignore:read_text is deprecated.*:DeprecationWarning",
     "ignore:open_binary is deprecated.*:DeprecationWarning",
+    "ignore:pkg_resources is deprecated as an API.*:DeprecationWarning",
 ]
 
 [tool.semantic_release]
diff --git a/script/actions_utils/generate_test_matrix.py b/script/actions_utils/generate_test_matrix.py
index 9810c91b7..53a6f5d55 100644
--- a/script/actions_utils/generate_test_matrix.py
+++ b/script/actions_utils/generate_test_matrix.py
@@ -22,6 +22,7 @@ class PythonVersion(enum.Enum):
     V_3_9 = "3.9"
     V_3_10 = "3.10"
     V_3_11 = "3.11"
+    V_3_12 = "3.12"
 
 
 class OS(enum.Enum):
diff --git a/script/actions_utils/refresh_notebooks_list.py b/script/actions_utils/refresh_notebooks_list.py
index 0ad74f75c..42fb6469c 100644
--- a/script/actions_utils/refresh_notebooks_list.py
+++ b/script/actions_utils/refresh_notebooks_list.py
@@ -171,7 +171,7 @@ def main(file_to_update):
 
     # If the script was called for checking if the list has correctly been updated, call an assert
     # without updating the file
-    if args.check:
+    if args.check:  # pylint: disable=possibly-used-before-assignment
         with open(file_to_update, "r", encoding="utf-8") as file:
             oldlines = file.readlines()
 
diff --git a/script/doc_utils/gen_supported_ops.py b/script/doc_utils/gen_supported_ops.py
index da93556e0..687a2544f 100644
--- a/script/doc_utils/gen_supported_ops.py
+++ b/script/doc_utils/gen_supported_ops.py
@@ -54,7 +54,7 @@ def main(file_to_update):
 
     # This code is seen as a duplicate with other scripts we have
     # pylint: disable=duplicate-code
-    if args.check:
+    if args.check:  # pylint: disable=possibly-used-before-assignment
 
         with open(file_to_update, "r", encoding="utf-8") as file:
             oldlines = file.readlines()
@@ -62,7 +62,7 @@ def main(file_to_update):
         assert (
             oldlines == newlines
         ), "List of supported functions is not up to date. Please run `make supported_ops`. "
-    # pylint: disable=duplicate-code
+    # pylint: enable=duplicate-code
 
     else:
         with open(file_to_update, "w", encoding="utf-8") as file:
diff --git a/script/make_utils/check_installation_with_all_python.sh b/script/make_utils/check_installation_with_all_python.sh
index 8aca76dfe..b20ab6ca3 100755
--- a/script/make_utils/check_installation_with_all_python.sh
+++ b/script/make_utils/check_installation_with_all_python.sh
@@ -25,7 +25,7 @@ do
             ;;
 
         "--all" )
-            VERSION_LIST="3.8 3.9 3.10 3.11"
+            VERSION_LIST="3.8 3.9 3.10 3.11 3.12"
             ;;
 
         "--version" )
@@ -44,7 +44,7 @@ done
 
 if [ "${VERSION_LIST}" == "" ]
 then
-    VERSION_LIST="3.8 3.9 3.10 3.11"
+    VERSION_LIST="3.8 3.9 3.10 3.11 3.12"
 fi
 
 for VERSION in $VERSION_LIST
diff --git a/script/make_utils/licenses.sh b/script/make_utils/licenses.sh
index 6c75717c8..3d2813d7e 100755
--- a/script/make_utils/licenses.sh
+++ b/script/make_utils/licenses.sh
@@ -98,6 +98,8 @@ then
         IS_CORRECT_PYTHON=1
     elif [[ "${CHECK_VERSION}" == *"3.11"* ]]; then
         IS_CORRECT_PYTHON=1
+    elif [[ "${CHECK_VERSION}" == *"3.12"* ]]; then
+        IS_CORRECT_PYTHON=1
     fi
 
     if [ $IS_CORRECT_PYTHON -eq 0 ]
diff --git a/script/make_utils/nbqa.sh b/script/make_utils/nbqa.sh
index cc7699cf9..944f094a9 100755
--- a/script/make_utils/nbqa.sh
+++ b/script/make_utils/nbqa.sh
@@ -28,7 +28,7 @@ function nbqa_ize()
         #       %matplotlib inline
         # --extend-ignore=DAR is because we don't want to run darglint
         poetry run nbqa flake8 "${NB}" --max-line-length 100 --per-file-ignores="__init__.py:F401" \
-            --ignore=E402,W503 --extend-ignore=DAR
+            --ignore=E402,W503,E226,E203 --extend-ignore=DAR
 
         # With some ignored errors, since we don't care:
         #       that the notebook filename is capitalized (invalid-name)
diff --git a/script/make_utils/setup_os_deps.sh b/script/make_utils/setup_os_deps.sh
index 45b81a2ff..c297089af 100755
--- a/script/make_utils/setup_os_deps.sh
+++ b/script/make_utils/setup_os_deps.sh
@@ -43,7 +43,7 @@ linux_install_gitleaks () {
         tar -xvf "${DOWNLOADED_FILE}" -C "${TMP_WORKDIR}"
         GITLEAKS_BIN="${TMP_WORKDIR}/gitleaks"
         chmod +x "${GITLEAKS_BIN}"
-        cp "${GITLEAKS_BIN}" /usr/local/bin/
+        ${SUDO_BIN:+$SUDO_BIN} cp "${GITLEAKS_BIN}" /usr/local/bin/
     else
         echo "Hash mismatch"
         echo "Got sha256:           ${SHA256_DOWNLOADED_FILE}"
@@ -69,7 +69,7 @@ linux_install_actionlint () {
         tar -xvf "${DOWNLOADED_FILE}" -C "${TMP_WORKDIR}"
         ACTIONLINT_BIN="${TMP_WORKDIR}/actionlint"
         chmod +x "${ACTIONLINT_BIN}"
-        cp "${ACTIONLINT_BIN}" /usr/local/bin/
+        ${SUDO_BIN:+$SUDO_BIN} cp "${ACTIONLINT_BIN}" /usr/local/bin/
     else
         echo "Hash mismatch"
         echo "Got sha256:           ${SHA256_DOWNLOADED_FILE}"
@@ -85,32 +85,27 @@ linux_install_github_cli () {
     # https://github.com/cli/cli/blob/trunk/docs/install_linux.md#debian-ubuntu-linux-raspberry-pi-os-apt
     echo "Installing github-CLI"
     export GH_CLI_VERSION="2.51.0"
-    wget https://github.com/cli/cli/releases/download/v${GH_CLI_VERSION}/gh_${GH_CLI_VERSION}_linux_amd64.deb
-    dpkg -i gh_${GH_CLI_VERSION}_linux_amd64.deb
+    wget -P /home/dev_user https://github.com/cli/cli/releases/download/v${GH_CLI_VERSION}/gh_${GH_CLI_VERSION}_linux_amd64.deb
+    ${SUDO_BIN:+$SUDO_BIN} dpkg -i /home/dev_user/gh_${GH_CLI_VERSION}_linux_amd64.deb
 }
 
 OS_NAME=$(uname)
 
 if [[ "${OS_NAME}" == "Linux" ]]; then
     # Docker build
-    if isDockerBuildkit || (isDocker && ! isDockerContainer); then
-        CLEAR_APT_LISTS="rm -rf /var/lib/apt/lists/* &&"
-        SUDO_BIN=""
-    else
-        CLEAR_APT_LISTS=""
-        SUDO_BIN="$(command -v sudo)"
-        if [[ "${SUDO_BIN}" != "" ]]; then
-            SUDO_BIN="${SUDO_BIN} "
-        fi
+    CLEAR_APT_LISTS=""
+    SUDO_BIN="$(command -v sudo)"
+    if [[ "${SUDO_BIN}" != "" ]]; then
+        SUDO_BIN="${SUDO_BIN} "
     fi
-
+   
     PYTHON_PACKAGES=
     if [[ "${LINUX_INSTALL_PYTHON}" == "1" ]]; then
         PYTHON_PACKAGES="python3-pip \
-        python3.8 \
-        python3.8-dev \
-        python3.8-tk \
-        python3.8-venv \
+        python3 \
+        python3-dev \
+        python3-tk \
+        python3-venv \
         python-is-python3 \
         "
     fi
@@ -119,7 +114,8 @@ if [[ "${OS_NAME}" == "Linux" ]]; then
     then
         SETUP_CMD="linux_install_actionlint"
     else
-        SETUP_CMD="${SUDO_BIN:+$SUDO_BIN}apt-get update && apt-get upgrade --no-install-recommends -y && \
+        SETUP_CMD="${SUDO_BIN:+$SUDO_BIN}apt-get update && \
+        ${SUDO_BIN:+$SUDO_BIN}apt-get upgrade --no-install-recommends -y && \
         ${SUDO_BIN:+$SUDO_BIN}apt-get install --no-install-recommends -y \
         build-essential \
         curl \
@@ -136,13 +132,22 @@ if [[ "${OS_NAME}" == "Linux" ]]; then
         openssl \
         shellcheck \
         texlive-latex-base texlive-latex-extra texlive-fonts-recommended texlive-xetex lmodern \
-        wget && \
+        wget pipx &&
         ${CLEAR_APT_LISTS:+$CLEAR_APT_LISTS} \
-        python3 -m pip install --no-cache-dir --upgrade pip && \
-        python3 -m pip install --no-cache-dir --ignore-installed poetry==1.7.1 && \
-        linux_install_gitleaks && linux_install_actionlint && linux_install_github_cli"
+        linux_install_gitleaks && \
+        linux_install_actionlint && \
+        linux_install_github_cli"
     fi
     eval "${SETUP_CMD}"
+
+    # Install poetry, either with pipx for ubuntu >= 23
+    # or through regular pip for older ubuntu
+    (pipx install poetry && pipx ensurepath) || \
+    (\
+        python3 -m pip install --no-cache-dir --upgrade pip && \
+        python3 -m pip install --no-cache-dir --ignore-installed poetry==1.7.1 \
+    );
+    echo "PATH=$PATH:/home/dev_user/.local/bin/" >> ~/.bashrc
 elif [[ "${OS_NAME}" == "Darwin" ]]; then
 
     # Some problems with the git which is preinstalled on AWS virtual machines. Let's unlink it
diff --git a/src/concrete/ml/deployment/_utils.py b/src/concrete/ml/deployment/_utils.py
index 835651b95..2a31f1779 100644
--- a/src/concrete/ml/deployment/_utils.py
+++ b/src/concrete/ml/deployment/_utils.py
@@ -7,7 +7,7 @@
 
 def serialize_encrypted_values(
     *values_enc: Optional[fhe.Value],
-) -> Union[Optional[bytes], Optional[Tuple[bytes]]]:
+) -> Union[Optional[bytes], Tuple[Optional[bytes], ...]]:
     """Serialize encrypted values.
 
     If a value is None, None is returned.
diff --git a/src/concrete/ml/pandas/__init__.py b/src/concrete/ml/pandas/__init__.py
index 4f402ea34..b61c35a58 100644
--- a/src/concrete/ml/pandas/__init__.py
+++ b/src/concrete/ml/pandas/__init__.py
@@ -1,7 +1,8 @@
 """Public API for encrypted data-frames."""
 
+from collections.abc import Hashable
 from pathlib import Path
-from typing import Hashable, Optional, Sequence, Tuple, Union
+from typing import Optional, Sequence, Tuple, Union
 
 from .client_engine import ClientEngine
 from .dataframe import EncryptedDataFrame
diff --git a/src/concrete/ml/pandas/_operators.py b/src/concrete/ml/pandas/_operators.py
index e044ef1bf..dbe2d14bf 100644
--- a/src/concrete/ml/pandas/_operators.py
+++ b/src/concrete/ml/pandas/_operators.py
@@ -1,6 +1,7 @@
 """Implement Pandas operators in FHE using encrypted data-frames."""
 
-from typing import Any, Dict, Hashable, List, Optional, Sequence, Tuple, Union
+from collections.abc import Hashable
+from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 
 import numpy
 import pandas
diff --git a/src/concrete/ml/pandas/dataframe.py b/src/concrete/ml/pandas/dataframe.py
index 8c7e6ef6c..90f90b6f3 100644
--- a/src/concrete/ml/pandas/dataframe.py
+++ b/src/concrete/ml/pandas/dataframe.py
@@ -1,8 +1,9 @@
 """Define the encrypted data-frame framework."""
 
 import json
+from collections.abc import Hashable
 from pathlib import Path
-from typing import Dict, Hashable, List, Optional, Sequence, Tuple, Union
+from typing import Dict, List, Optional, Sequence, Tuple, Union
 from zipfile import ZIP_STORED, ZipFile
 
 import numpy
diff --git a/src/concrete/ml/sklearn/base.py b/src/concrete/ml/sklearn/base.py
index ed08841bc..fa692b2ec 100644
--- a/src/concrete/ml/sklearn/base.py
+++ b/src/concrete/ml/sklearn/base.py
@@ -1572,6 +1572,29 @@ def post_processing(self, y_preds: numpy.ndarray) -> numpy.ndarray:
 
         return super().post_processing(y_preds)
 
+    def get_sklearn_params(self, deep: bool = True) -> dict:
+        """Get parameters for this estimator.
+
+        This method is used to instantiate a scikit-learn model using the Concrete ML model's
+        parameters. It does not override scikit-learn's existing `get_params` method in order to
+        not break its implementation of `set_params`.
+
+        Args:
+            deep (bool): If True, will return the parameters for this estimator and contained
+                subobjects that are estimators. Default to True.
+
+        Returns:
+            params (dict): Parameter names mapped to their values.
+        """
+        # pylint: disable-next=no-member
+        params = super().get_params(deep=deep)  # type: ignore[misc]
+
+        params.pop("n_bits", None)
+        if "1.1." in sklearn.__version__:
+            params.pop("monotonic_cst", None)
+
+        return params
+
 
 class BaseTreeRegressorMixin(BaseTreeEstimatorMixin, sklearn.base.RegressorMixin, ABC):
     """Mixin class for tree-based regressors.
@@ -1668,6 +1691,12 @@ def from_sklearn_model(
         # Extract scikit-learn's initialization parameters
         init_params = sklearn_model.get_params()
 
+        # Ensure compatibility for both sklearn 1.1 and >=1.4
+        # This parameter was removed in 1.4. If this package is installed
+        # with sklearn 1.1 which has it, then remove it when
+        # instantiating the 1.4 API compatible Concrete ML model
+        init_params.pop("normalize", None)
+
         # Instantiate the Concrete ML model and update initialization parameters
         # This update is necessary as we currently store scikit-learn attributes in Concrete ML
         # classes during initialization (for example: link or power attributes in GLMs)
diff --git a/src/concrete/ml/sklearn/glm.py b/src/concrete/ml/sklearn/glm.py
index 4779d66ec..cd034f43e 100644
--- a/src/concrete/ml/sklearn/glm.py
+++ b/src/concrete/ml/sklearn/glm.py
@@ -6,6 +6,7 @@
 from typing import Any, Dict, Union
 
 import numpy
+import sklearn
 import sklearn.linear_model
 
 from ..common.debugging.custom_assert import assert_true
@@ -136,6 +137,19 @@ def load_dict(cls, metadata: Dict):
 
         return obj
 
+    def get_sklearn_params(self, deep: bool = True) -> dict:
+        # Here, the `get_params` method is the `BaseEstimator.get_params` method from scikit-learn
+        # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/3373
+        params = super().get_params(deep=deep)  # type: ignore[misc]
+
+        # Remove the parameters added by Concrete ML
+        params.pop("n_bits", None)
+        # Remove sklearn 1.4 parameter when using sklearn 1.1
+        if "1.1." in sklearn.__version__:
+            params.pop("solver", None)
+
+        return params
+
 
 class PoissonRegressor(_GeneralizedLinearRegressor):
     """A Poisson regression model with FHE.
@@ -156,12 +170,13 @@ class PoissonRegressor(_GeneralizedLinearRegressor):
     sklearn_model_class = sklearn.linear_model.PoissonRegressor
     _is_a_public_cml_model = True
 
-    def __init__(
+    def __init__(  # pylint: disable=useless-parent-delegation
         self,
         *,
         n_bits: Union[int, dict] = 8,
         alpha: float = 1.0,
         fit_intercept: bool = True,
+        solver="lbfgs",
         max_iter: int = 100,
         tol: float = 1e-4,
         warm_start: bool = False,
@@ -171,6 +186,7 @@ def __init__(
             n_bits=n_bits,
             alpha=alpha,
             fit_intercept=fit_intercept,
+            solver=solver,
             max_iter=max_iter,
             tol=tol,
             warm_start=warm_start,
@@ -200,12 +216,13 @@ class GammaRegressor(_GeneralizedLinearRegressor):
     sklearn_model_class = sklearn.linear_model.GammaRegressor
     _is_a_public_cml_model = True
 
-    def __init__(
+    def __init__(  # pylint: disable=useless-parent-delegation
         self,
         *,
         n_bits: Union[int, dict] = 8,
         alpha: float = 1.0,
         fit_intercept: bool = True,
+        solver="lbfgs",
         max_iter: int = 100,
         tol: float = 1e-4,
         warm_start: bool = False,
@@ -215,6 +232,7 @@ def __init__(
             n_bits=n_bits,
             alpha=alpha,
             fit_intercept=fit_intercept,
+            solver=solver,
             max_iter=max_iter,
             tol=tol,
             warm_start=warm_start,
@@ -245,7 +263,7 @@ class TweedieRegressor(_GeneralizedLinearRegressor):
     sklearn_model_class = sklearn.linear_model.TweedieRegressor
     _is_a_public_cml_model = True
 
-    def __init__(
+    def __init__(  # pylint: disable=too-many-arguments
         self,
         *,
         n_bits: Union[int, dict] = 8,
@@ -253,6 +271,7 @@ def __init__(
         alpha: float = 1.0,
         fit_intercept: bool = True,
         link: str = "auto",
+        solver="lbfgs",
         max_iter: int = 100,
         tol: float = 1e-4,
         warm_start: bool = False,
@@ -262,6 +281,7 @@ def __init__(
             n_bits=n_bits,
             alpha=alpha,
             fit_intercept=fit_intercept,
+            solver=solver,
             max_iter=max_iter,
             tol=tol,
             warm_start=warm_start,
diff --git a/src/concrete/ml/sklearn/linear_model.py b/src/concrete/ml/sklearn/linear_model.py
index 768748e34..4a86360cc 100644
--- a/src/concrete/ml/sklearn/linear_model.py
+++ b/src/concrete/ml/sklearn/linear_model.py
@@ -51,7 +51,6 @@ def __init__(
         self,
         n_bits=8,
         fit_intercept=True,
-        normalize="deprecated",
         copy_X=True,
         n_jobs=None,
         positive=False,
@@ -60,7 +59,6 @@ def __init__(
         super().__init__(n_bits=n_bits)
 
         self.fit_intercept = fit_intercept
-        self.normalize = normalize
         self.copy_X = copy_X
         self.n_jobs = n_jobs
         self.positive = positive
@@ -85,7 +83,6 @@ def dump_dict(self) -> Dict[str, Any]:
 
         # scikit-learn
         metadata["fit_intercept"] = self.fit_intercept
-        metadata["normalize"] = self.normalize
         metadata["copy_X"] = self.copy_X
         metadata["n_jobs"] = self.n_jobs
         metadata["positive"] = self.positive
@@ -112,7 +109,6 @@ def load_dict(cls, metadata: Dict):
 
         # scikit-learn
         obj.fit_intercept = metadata["fit_intercept"]
-        obj.normalize = metadata["normalize"]
         obj.copy_X = metadata["copy_X"]
         obj.n_jobs = metadata["n_jobs"]
         obj.positive = metadata["positive"]
@@ -1147,7 +1143,7 @@ class SGDRegressor(SklearnSGDRegressorMixin):
 
     _is_a_public_cml_model = True
 
-    def __init__(
+    def __init__(  # pylint: disable=too-many-arguments
         self,
         n_bits=8,
         loss="squared_error",
@@ -1302,7 +1298,6 @@ def __init__(
         alpha=1.0,
         l1_ratio=0.5,
         fit_intercept=True,
-        normalize="deprecated",
         precompute=False,
         max_iter=1000,
         copy_X=True,
@@ -1318,7 +1313,6 @@ def __init__(
         self.alpha = alpha
         self.l1_ratio = l1_ratio
         self.fit_intercept = fit_intercept
-        self.normalize = normalize
         self.copy_X = copy_X
         self.positive = positive
         self.precompute = precompute
@@ -1350,7 +1344,6 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["alpha"] = self.alpha
         metadata["l1_ratio"] = self.l1_ratio
         metadata["fit_intercept"] = self.fit_intercept
-        metadata["normalize"] = self.normalize
         metadata["copy_X"] = self.copy_X
         metadata["positive"] = self.positive
         metadata["precompute"] = self.precompute
@@ -1384,7 +1377,6 @@ def load_dict(cls, metadata: Dict):
         obj.alpha = metadata["alpha"]
         obj.l1_ratio = metadata["l1_ratio"]
         obj.fit_intercept = metadata["fit_intercept"]
-        obj.normalize = metadata["normalize"]
         obj.copy_X = metadata["copy_X"]
         obj.positive = metadata["positive"]
         obj.precompute = metadata["precompute"]
@@ -1422,7 +1414,6 @@ def __init__(
         n_bits=8,
         alpha: float = 1.0,
         fit_intercept=True,
-        normalize="deprecated",
         precompute=False,
         copy_X=True,
         max_iter=1000,
@@ -1437,7 +1428,6 @@ def __init__(
 
         self.alpha = alpha
         self.fit_intercept = fit_intercept
-        self.normalize = normalize
         self.copy_X = copy_X
         self.positive = positive
         self.max_iter = max_iter
@@ -1468,7 +1458,6 @@ def dump_dict(self) -> Dict[str, Any]:
         # scikit-learn
         metadata["alpha"] = self.alpha
         metadata["fit_intercept"] = self.fit_intercept
-        metadata["normalize"] = self.normalize
         metadata["copy_X"] = self.copy_X
         metadata["positive"] = self.positive
         metadata["max_iter"] = self.max_iter
@@ -1501,7 +1490,6 @@ def load_dict(cls, metadata: Dict):
         # scikit-learn
         obj.alpha = metadata["alpha"]
         obj.fit_intercept = metadata["fit_intercept"]
-        obj.normalize = metadata["normalize"]
         obj.copy_X = metadata["copy_X"]
         obj.positive = metadata["positive"]
         obj.max_iter = metadata["max_iter"]
@@ -1539,10 +1527,9 @@ def __init__(
         n_bits=8,
         alpha: float = 1.0,
         fit_intercept=True,
-        normalize="deprecated",
         copy_X=True,
         max_iter=None,
-        tol=0.001,
+        tol=0.0001,
         solver="auto",
         positive=False,
         random_state=None,
@@ -1552,7 +1539,6 @@ def __init__(
 
         self.alpha = alpha
         self.fit_intercept = fit_intercept
-        self.normalize = normalize
         self.copy_X = copy_X
         self.positive = positive
         self.max_iter = max_iter
@@ -1581,7 +1567,6 @@ def dump_dict(self) -> Dict[str, Any]:
         # scikit-learn
         metadata["alpha"] = self.alpha
         metadata["fit_intercept"] = self.fit_intercept
-        metadata["normalize"] = self.normalize
         metadata["copy_X"] = self.copy_X
         metadata["positive"] = self.positive
         metadata["max_iter"] = self.max_iter
@@ -1612,7 +1597,6 @@ def load_dict(cls, metadata: Dict):
         # scikit-learn
         obj.alpha = metadata["alpha"]
         obj.fit_intercept = metadata["fit_intercept"]
-        obj.normalize = metadata["normalize"]
         obj.copy_X = metadata["copy_X"]
         obj.positive = metadata["positive"]
         obj.max_iter = metadata["max_iter"]
diff --git a/src/concrete/ml/sklearn/rf.py b/src/concrete/ml/sklearn/rf.py
index 2d7d98bdd..a3181d12a 100644
--- a/src/concrete/ml/sklearn/rf.py
+++ b/src/concrete/ml/sklearn/rf.py
@@ -39,6 +39,7 @@ def __init__(
         class_weight=None,
         ccp_alpha=0.0,
         max_samples=None,
+        monotonic_cst=None,
     ):
         """Initialize the RandomForestClassifier.
 
@@ -65,6 +66,7 @@ def __init__(
         self.max_leaf_nodes = max_leaf_nodes
         self.min_impurity_decrease = min_impurity_decrease
         self.ccp_alpha = ccp_alpha
+        self.monotonic_cst = monotonic_cst
 
     def post_processing(self, y_preds: numpy.ndarray) -> numpy.ndarray:
         # Here, we want to use BaseTreeEstimatorMixin's `post-processing` method as
@@ -106,6 +108,7 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["max_leaf_nodes"] = self.max_leaf_nodes
         metadata["min_impurity_decrease"] = self.min_impurity_decrease
         metadata["ccp_alpha"] = self.ccp_alpha
+        metadata["monotonic_cst"] = self.monotonic_cst
 
         return metadata
 
@@ -151,6 +154,7 @@ def load_dict(cls, metadata: Dict):
         obj.max_leaf_nodes = metadata["max_leaf_nodes"]
         obj.min_impurity_decrease = metadata["min_impurity_decrease"]
         obj.ccp_alpha = metadata["ccp_alpha"]
+        obj.monotonic_cst = metadata["monotonic_cst"]
 
         return obj
 
@@ -184,6 +188,7 @@ def __init__(
         warm_start=False,
         ccp_alpha=0.0,
         max_samples=None,
+        monotonic_cst=None,
     ):
         """Initialize the RandomForestRegressor.
 
@@ -209,6 +214,7 @@ def __init__(
         self.max_leaf_nodes = max_leaf_nodes
         self.min_impurity_decrease = min_impurity_decrease
         self.ccp_alpha = ccp_alpha
+        self.monotonic_cst = monotonic_cst
 
     def dump_dict(self) -> Dict[str, Any]:
         metadata: Dict[str, Any] = {}
@@ -243,6 +249,7 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["max_leaf_nodes"] = self.max_leaf_nodes
         metadata["min_impurity_decrease"] = self.min_impurity_decrease
         metadata["ccp_alpha"] = self.ccp_alpha
+        metadata["monotonic_cst"] = self.monotonic_cst
 
         return metadata
 
@@ -288,5 +295,6 @@ def load_dict(cls, metadata: Dict):
         obj.max_leaf_nodes = metadata["max_leaf_nodes"]
         obj.min_impurity_decrease = metadata["min_impurity_decrease"]
         obj.ccp_alpha = metadata["ccp_alpha"]
+        obj.monotonic_cst = metadata["monotonic_cst"]
 
         return obj
diff --git a/src/concrete/ml/sklearn/tree.py b/src/concrete/ml/sklearn/tree.py
index 42965cdce..fba10ca3f 100644
--- a/src/concrete/ml/sklearn/tree.py
+++ b/src/concrete/ml/sklearn/tree.py
@@ -32,6 +32,7 @@ def __init__(
         min_impurity_decrease=0.0,
         class_weight=None,
         ccp_alpha: float = 0.0,
+        monotonic_cst=None,
         n_bits: Union[int, Dict[str, int]] = 6,
     ):
         """Initialize the DecisionTreeClassifier.
@@ -54,6 +55,7 @@ def __init__(
         self.random_state = random_state
         self.min_impurity_decrease = min_impurity_decrease
         self.ccp_alpha = ccp_alpha
+        self.monotonic_cst = monotonic_cst
 
     def __getattr__(self, attr: str):
         # We directly expose the following methods as they are commonly used with decision trees
@@ -100,6 +102,7 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["random_state"] = self.random_state
         metadata["min_impurity_decrease"] = self.min_impurity_decrease
         metadata["ccp_alpha"] = self.ccp_alpha
+        metadata["monotonic_cst"] = self.monotonic_cst
 
         return metadata
 
@@ -140,6 +143,7 @@ def load_dict(cls, metadata: Dict):
         obj.random_state = metadata["random_state"]
         obj.min_impurity_decrease = metadata["min_impurity_decrease"]
         obj.ccp_alpha = metadata["ccp_alpha"]
+        obj.monotonic_cst = metadata["monotonic_cst"]
 
         return obj
 
@@ -166,6 +170,7 @@ def __init__(
         max_leaf_nodes=None,
         min_impurity_decrease=0.0,
         ccp_alpha=0.0,
+        monotonic_cst=None,
         n_bits: Union[int, Dict[str, int]] = 6,
     ):
         """Initialize the DecisionTreeRegressor.
@@ -187,6 +192,7 @@ def __init__(
         self.random_state = random_state
         self.min_impurity_decrease = min_impurity_decrease
         self.ccp_alpha = ccp_alpha
+        self.monotonic_cst = monotonic_cst
 
     def __getattr__(self, attr: str):
         # We directly expose the following methods as they are commonly used with decision trees
@@ -226,6 +232,7 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["random_state"] = self.random_state
         metadata["min_impurity_decrease"] = self.min_impurity_decrease
         metadata["ccp_alpha"] = self.ccp_alpha
+        metadata["monotonic_cst"] = self.monotonic_cst
 
         return metadata
 
@@ -266,5 +273,6 @@ def load_dict(cls, metadata: Dict):
         obj.random_state = metadata["random_state"]
         obj.min_impurity_decrease = metadata["min_impurity_decrease"]
         obj.ccp_alpha = metadata["ccp_alpha"]
+        obj.monotonic_cst = metadata["monotonic_cst"]
 
         return obj
diff --git a/tests/onnx/test_onnx_graph_manipulations.py b/tests/onnx/test_onnx_graph_manipulations.py
index 4507e0f71..202ea8a70 100644
--- a/tests/onnx/test_onnx_graph_manipulations.py
+++ b/tests/onnx/test_onnx_graph_manipulations.py
@@ -43,7 +43,7 @@ def test_remove_unused_constant_nodes():
 
     output = helper.make_tensor_value_info("used_constant", onnx.TensorProto.FLOAT, ())
     output_2 = helper.make_tensor_value_info("other_used_constant", onnx.TensorProto.FLOAT, ())
-    identity_output = helper.make_tensor_value_info("indentity", onnx.TensorProto.FLOAT, ())
+    identity_output = helper.make_tensor_value_info("identity", onnx.TensorProto.FLOAT, ())
 
     graph_def = helper.make_graph(
         nodes=[unused_constant, used_constant, other_used_constant, identity_node],
diff --git a/tests/quantization/test_quantized_ops.py b/tests/quantization/test_quantized_ops.py
index 59e92b8fa..5c79c9270 100644
--- a/tests/quantization/test_quantized_ops.py
+++ b/tests/quantization/test_quantized_ops.py
@@ -130,8 +130,7 @@ def quantized_op_implements_analytical_calibration(op_instance):
 
     # Can only be called on ops that do not overload the calibrate_analytical_output function
     assert (
-        type(op_instance.calibrate_analytical_output)  # pylint: disable=unidiomatic-typecheck
-        != QuantizedMixingOp.calibrate_analytical_output
+        op_instance.calibrate_analytical_output is not QuantizedMixingOp.calibrate_analytical_output
     )
 
     with pytest.raises(AssertionError, match=".*calibrate_analytical_output.*"):
@@ -913,9 +912,9 @@ def test_quantized_avg_pool(params, n_bits, is_signed, check_r2_score, check_flo
 
     # Compute the torch average pool
     bceil_mode = bool(ceil_mode)
-    torch_res = torch.nn.functional.avg_pool2d(
+    torch_res = torch.nn.functional.avg_pool2d(  # pylint: disable=not-callable
         tx_pad, kernel_shape, strides, 0, bceil_mode
-    ).numpy()  # pylint: disable=not-callable
+    ).numpy()
     check_float_array_equal(torch_res, expected_result)
 
     # Compute the quantized result
diff --git a/tests/sklearn/test_sklearn_models.py b/tests/sklearn/test_sklearn_models.py
index 13cb80931..5f500d4de 100644
--- a/tests/sklearn/test_sklearn_models.py
+++ b/tests/sklearn/test_sklearn_models.py
@@ -36,6 +36,7 @@
 import numpy
 import pandas
 import pytest
+import sklearn
 import torch
 from sklearn.decomposition import PCA
 from sklearn.exceptions import ConvergenceWarning, UndefinedMetricWarning
@@ -2251,7 +2252,17 @@ def test_error_raise_unsupported_pandas_values(model_class, bad_value, expected_
 def test_initialization_variables_and_defaults_match(
     model_class, parameters, load_data, is_weekly_option
 ):
-    """Test CML models init parameters and default values vs scikit-learn models."""
+    """Test CML models init parameters and default values vs scikit-learn models.
+
+    Concrete ML currently implements sklearn 1.4 API so skip this test of the
+    sklearn version differs.
+    """
+    if "1.1." in sklearn.__version__:
+        pytest.skip(
+            "Concrete ML currently implements sklearn 1.4 API"
+            f" skipping this test on version {sklearn.__version__}"
+        )
+
     n_bits = get_n_bits_non_correctness(model_class)
 
     model_name = get_model_name(model_class)
@@ -2318,6 +2329,9 @@ def is_nan(x):
             sklearn_params_defaults[param] == cml_params_defaults[param]
             # Some parameter can be nan which can't be compared using equality
             or (is_nan(sklearn_params_defaults[param]) and is_nan(cml_params_defaults[param]))
+            # Some parameters can default to 'warn' in sklearn in order to issue a FutureWraning
+            # but the actual default is not 'warn', the actula one is given in the docstring
+            or sklearn_params_defaults[param] == "warn"
         )
     }
 
diff --git a/tests/torch/test_compile_torch.py b/tests/torch/test_compile_torch.py
index 340618b47..202def064 100644
--- a/tests/torch/test_compile_torch.py
+++ b/tests/torch/test_compile_torch.py
@@ -1213,7 +1213,7 @@ def test_torch_padding(default_configuration, check_circuit_has_no_tlu):
     torch_output = torch_output > 0
     cml_output = cml_output > 0
 
-    assert numpy.alltrue(torch_output == cml_output)
+    assert numpy.all(torch_output == cml_output)
     check_circuit_has_no_tlu(quant_model.fhe_circuit)