### Download Example Dataset

Source: https://github.com/cleanlab/cleanvision/blob/main/README.md

Downloads an example dataset archive for testing CleanVision. This command fetches a zip file containing sample images.

```shell
wget -nc 'https://cleanlab-public.s3.amazonaws.com/CleanVision/image_files.zip'
```

--------------------------------

### Install Project Dependencies

Source: https://github.com/cleanlab/cleanvision/blob/main/DEVELOPMENT.md

Commands to upgrade pip, install the package in editable mode with all extras, and install development-specific requirements.

```shell
python -m pip install --upgrade pip
pip install -e ".[all]"
pip install -r requirements-dev.txt
deactivate
source ./ENV/bin/activate
```

--------------------------------

### Install CleanVision with Hugging Face Support

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/huggingface_dataset.ipynb

Installs the necessary packages for using CleanVision with Hugging Face datasets. It's recommended to restart the notebook runtime after installation.

```bash
!pip install -U pip
!pip install "cleanvision[huggingface] @ git+https://github.com/cleanlab/cleanvision.git"
```

--------------------------------

### Install CleanVision and Dependencies

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/torchvision_dataset.ipynb

Installs the necessary Python packages for CleanVision with PyTorch support. Ensure the notebook runtime is restarted after installation.

```python
!pip install -U pip
!pip install "cleanvision[pytorch] @ git+https://github.com/cleanlab/cleanvision.git"
```

--------------------------------

### Execute Complete Image Quality Workflow

Source: https://context7.com/cleanlab/cleanvision/llms.txt

A full end-to-end example covering initialization, custom issue detection configuration, result reporting, and exporting data for removal.

```python
from cleanvision import Imagelab
import pandas as pd

imagelab = Imagelab(data_path="./my_dataset/")
imagelab.find_issues(issue_types={"dark": {"threshold": 0.25}, "exact_duplicates": {}}, n_jobs=4)
imagelab.report()
issues_df = imagelab.issues
imagelab.save("./imagelab_checkpoint")
issues_df.to_csv("./image_quality_report.csv")
```

--------------------------------

### Install CleanVision Package

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/tutorial.ipynb

Commands to install the CleanVision library via pip. It is recommended to restart the notebook runtime after installation.

```python
!pip install -U pip
!pip install git+https://github.com/cleanlab/cleanvision.git
```

--------------------------------

### Build Documentation

Source: https://github.com/cleanlab/cleanvision/blob/main/DEVELOPMENT.md

Commands to install documentation dependencies and build the project documentation using Sphinx.

```shell
pip install -r docs/requirements.txt
sphinx-build docs/source cleanvision-docs
```

--------------------------------

### Install CleanVision Package

Source: https://github.com/cleanlab/cleanvision/blob/main/README.md

Installs the CleanVision package using pip. This is the first step to using the library for image dataset auditing.

```shell
pip install cleanvision
```

--------------------------------

### Setup Virtual Environment

Source: https://github.com/cleanlab/cleanvision/blob/main/DEVELOPMENT.md

Commands to create and activate a Python virtual environment using venv to isolate project dependencies.

```shell
python3 -m venv ./ENV
source ./ENV/bin/activate
```

--------------------------------

### Download and Extract Example Dataset

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/tutorial.ipynb

Commands to download a sample image dataset from S3 and extract the contents for processing.

```python
!wget - nc 'https://cleanlab-public.s3.amazonaws.com/CleanVision/image_files.zip'
!unzip -q image_files.zip
```

```shell
wget - nc 'https://cleanlab-public.s3.amazonaws.com/CleanVision/image_files.zip'
unzip -q image_files.zip
```

--------------------------------

### Install CleanVision via pip or source

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/index.rst

Commands to install the CleanVision package using pip or directly from the GitHub repository, including options for all dependencies.

```bash
pip install cleanvision
pip install "cleanvision[all]"
pip install git+https://github.com/cleanlab/cleanvision.git
pip install "git+https://github.com/cleanlab/cleanvision.git#egg=cleanvision[all]"
```

--------------------------------

### Install CleanVision Library

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Commands to install the CleanVision package via pip, including an option for installing all optional dependencies for broader compatibility.

```bash
pip install cleanvision
pip install "cleanvision[all]"
```

--------------------------------

### Configure Pre-commit Hooks

Source: https://github.com/cleanlab/cleanvision/blob/main/DEVELOPMENT.md

Command to install pre-commit git hooks for automated style checking.

```shell
pre-commit install
```

--------------------------------

### Visualize Specific Image Issues in Python

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/tutorial.ipynb

This code demonstrates how to use the `imagelab.visualize` function to display examples of specific image issues like 'grayscale'. It allows control over the number of examples and the size of images in the visualization grid.

```python
issue_types = ["grayscale"]
imagelab.visualize(issue_types=issue_types, num_images=3, cell_size=(3, 3))
```

--------------------------------

### HuggingFace Dataset Integration

Source: https://context7.com/cleanlab/cleanvision/llms.txt

An example demonstrating how to use CleanVision with HuggingFace datasets.

```APIDOC
## HuggingFace Dataset Integration

Full example of using CleanVision with HuggingFace datasets.

### Description
This example shows how to load datasets from HuggingFace and integrate them with CleanVision for image analysis.

### Method
N/A (Integration example)

### Endpoint
N/A

### Parameters
None specific to this integration example, relies on parameters of `datasets` and `cleanvision` libraries.

### Request Example
```python
from datasets import load_dataset, concatenate_datasets
from cleanvision import Imagelab

# Load a dataset from HuggingFace (example)
dataset = load_dataset("cats_vs_dogs", split="train")

# You might need to adapt the dataset structure to be compatible with Imagelab
# For example, if images are stored as file paths or PIL Images

# Assuming you have a way to get image paths or PIL Images from the dataset
# Example: Convert dataset to a list of image paths (replace with actual logic)
image_paths = [item['image_path'] for item in dataset]

imagelab = Imagelab(data_path=image_paths)
imagelab.find_issues()
imagelab.report()
```

### Response
#### Success Response (200)
CleanVision processes the images from the HuggingFace dataset, and results are available in `imagelab.issues` and `imagelab.summary`.

#### Response Example
(Standard CleanVision report output)
```

--------------------------------

### Persist Imagelab Results with save() and load()

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Provides examples of using the `save()` and `load()` methods to persist Imagelab instances. This is useful for avoiding recomputation of issues on large datasets. It covers saving results to disk, overwriting existing files, and loading previously saved results.

```python
from cleanvision import Imagelab

# Create and run issue detection
imagelab = Imagelab(data_path="/path/to/images/")
imagelab.find_issues()

# Save to disk
imagelab.save("./imagelab_results")

# Save with overwrite if folder exists
imagelab.save("./imagelab_results", force=True)

# Load previously saved results
loaded_imagelab = Imagelab.load("./imagelab_results")

# Access loaded results
print(loaded_imagelab.issue_summary)
print(loaded_imagelab.issues.head())

# Note: The data_path must remain unchanged for loaded Imagelab to function properly
```

--------------------------------

### Get Default Issue Types with Imagelab.list_default_issue_types()

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Shows how to retrieve the list of issue types that are checked by default when the `find_issues()` method is called without specifying any particular issue types.

```python
from cleanvision import Imagelab

# Get the list of default issue types
default_issues = Imagelab.list_default_issue_types()
print(default_issues)
```

--------------------------------

### Initialize Imagelab with Various Data Sources

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Demonstrates how to instantiate the Imagelab class using different input sources such as local directories, file lists, HuggingFace datasets, and Torchvision datasets.

```python
from cleanvision import Imagelab
from datasets import load_dataset
from torchvision.datasets import CIFAR10

imagelab = Imagelab(data_path="/path/to/images/")
filepaths = ["./images/img1.jpg", "./images/img2.png", "./images/img3.jpeg"]
imagelab = Imagelab(filepaths=filepaths)
dataset = load_dataset("cifar10", split="train")
imagelab = Imagelab(hf_dataset=dataset, image_key="img")
torch_dataset = CIFAR10(root="./data", download=True)
imagelab = Imagelab(torchvision_dataset=torch_dataset)
```

--------------------------------

### Execute Custom Issue Detection with Imagelab

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/tutorial.ipynb

Demonstrates how to initialize Imagelab, verify the registration of the custom issue manager, and run the detection process.

```python
imagelab = Imagelab(data_path=dataset_path)
issue_name = CustomIssueManager.issue_name
imagelab.list_possible_issue_types()

issue_types = {issue_name: {}}
imagelab.find_issues(issue_types)
imagelab.report()
```

--------------------------------

### Initializing Imagelab with HuggingFace datasets

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/faq.rst

Shows how to initialize the Imagelab object using a HuggingFace dataset by specifying the image_key.

```python
imagelab = Imagelab(hf_dataset=dataset, image_key="image")
```

--------------------------------

### Integrate CleanVision with HuggingFace Datasets

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Demonstrates how to load a HuggingFace dataset, concatenate splits, and initialize Imagelab for automated issue detection.

```python
from datasets import load_dataset, concatenate_datasets
from cleanvision import Imagelab

dataset_dict = load_dataset("cifar10")
dataset = concatenate_datasets([d for d in dataset_dict.values()])
imagelab = Imagelab(hf_dataset=dataset, image_key="img")
imagelab.find_issues()
imagelab.report()
```

--------------------------------

### Imagelab.list_default_issue_types() - Get Default Issue Types

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Retrieves a list of default issue types that CleanVision can detect.

```APIDOC
## Imagelab.list_default_issue_types()

### Description
Retrieves a list of default issue types that CleanVision can detect.

### Method
GET

### Endpoint
/imagelab/default_issue_types

### Parameters
None

### Request Example
```python
from cleanvision import Imagelab

default_types = Imagelab.list_default_issue_types()
print(default_types)
```

### Response
#### Success Response (200)
- **types** (list[string]) - A list of default issue type names.

#### Response Example
```json
{
  "types": ["dark", "light", "odd_aspect_ratio", "low_information", "exact_duplicates", "near_duplicates", "blurry", "grayscale", "odd_size"]
}
```
```

--------------------------------

### Integrate with Hugging Face Datasets

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/index.rst

Demonstrates how to load a Hugging Face dataset and use it with CleanVision for automated issue detection.

```python
from datasets import load_dataset, concatenate_datasets
dataset_dict = load_dataset("cifar10")
dataset = concatenate_datasets([d for d in dataset_dict.values()])
imagelab = Imagelab(hf_dataset=dataset, image_key="img")
imagelab.find_issues()
imagelab.report()
```

--------------------------------

### POST /imagelab/initialize

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/faq.rst

Initializes the Imagelab object with a dataset, supporting local paths, HuggingFace datasets, or Torchvision datasets.

```APIDOC
## POST /imagelab/initialize

### Description
Initializes the Imagelab environment with the target dataset.

### Method
POST

### Endpoint
/imagelab/initialize

### Parameters
#### Request Body
- **data_path** (string) - Optional - Local path to the image directory.
- **hf_dataset** (object) - Optional - HuggingFace dataset object.
- **image_key** (string) - Required - The key used to access image data within the dataset object.

### Request Example
{
  "hf_dataset": "dataset_object",
  "image_key": "image"
}

### Response
#### Success Response (200)
- **initialized** (boolean) - Returns true if the dataset was successfully loaded.
```

--------------------------------

### Issue Types Reference and Usage

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Details on various issue types that CleanVision can detect, along with their default parameters and an example of how to use them.

```APIDOC
## Issue Types Reference

CleanVision detects the following issue types with configurable thresholds:

### Description
This section details the available issue types in CleanVision, their default parameters, and how to configure them for issue detection.

### Method
N/A (Configuration example)

### Endpoint
N/A

### Parameters
#### Image Property Issues (Threshold-based)
- **dark** (dict) - Underexposed images. Default threshold: 0.32.
- **light** (dict) - Overexposed images. Default threshold: 0.05.
- **blurry** (dict) - Out of focus images. Default threshold: 0.29.
- **low_information** (dict) - Low entropy images. Default threshold: 0.3.
- **odd_aspect_ratio** (dict) - Unusual width/height ratio. Default threshold: 0.35.
- **grayscale** (dict) - Non-color images. No threshold, detects any grayscale image.
- **odd_size** (dict) - Abnormally sized images. Default parameter: `iqr_factor=3.0`.

#### Duplicate Detection Issues
- **exact_duplicates** (dict) - Detects identical images. Default parameter: `hash_type='md5'`.
- **near_duplicates** (dict) - Detects visually similar images. Default parameters: `hash_type='phash'`, `hash_size=8`.

### Request Example
```python
from cleanvision import Imagelab

imagelab = Imagelab(data_path="/path/to/images/")

issue_types = {
    "dark": {"threshold": 0.32},
    "light": {"threshold": 0.05},
    "blurry": {"threshold": 0.29},
    "low_information": {"threshold": 0.3},
    "odd_aspect_ratio": {"threshold": 0.35},
    "grayscale": {},
    "odd_size": {"iqr_factor": 3.0},
    "exact_duplicates": {"hash_type": "md5"},
    "near_duplicates": {
        "hash_type": "phash",
        "hash_size": 8
    }
}

imagelab.find_issues(issue_types=issue_types)
```

### Response
#### Success Response (200)
This method modifies the `imagelab` object in place. Results are accessed via `imagelab.issues` and `imagelab.summary`.

#### Response Example
(See `imagelab.issues` and `imagelab.summary` for detailed results)
```

--------------------------------

### Imagelab.list_possible_issue_types() - Get All Available Issue Types

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Retrieves a list of all available issue types, including any custom issue types that have been registered.

```APIDOC
## Imagelab.list_possible_issue_types()

### Description
Retrieves a list of all available issue types, including any custom issue types that have been registered.

### Method
GET

### Endpoint
/imagelab/possible_issue_types

### Parameters
None

### Request Example
```python
from cleanvision import Imagelab

all_types = Imagelab.list_possible_issue_types()
print(all_types)
```

### Response
#### Success Response (200)
- **types** (list[string]) - A list of all available issue type names.

#### Response Example
```json
{
  "types": ["dark", "light", "odd_aspect_ratio", "low_information", "exact_duplicates", "near_duplicates", "blurry", "grayscale", "odd_size", "custom_issue_1", "custom_issue_2"]
}
```
```

--------------------------------

### Integrate with Torchvision Datasets

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/index.rst

Shows how to combine Torchvision datasets and pass them to Imagelab for auditing.

```python
from torchvision.datasets import CIFAR10
from torch.utils.data import ConcatDataset
train_set = CIFAR10(root="./", download=True)
test_set = CIFAR10(root="./", train=False, download=True)
dataset = ConcatDataset([train_set, test_set])
imagelab = Imagelab(torchvision_dataset=dataset)
imagelab.find_issues()
imagelab.report()
```

--------------------------------

### Imagelab Initialization and Issue Detection

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/index.rst

The core API for initializing the Imagelab object with a dataset source and running automated issue detection.

```APIDOC
## Imagelab Initialization

### Description
Initializes the Imagelab object to audit image data from various sources.

### Method
Class Constructor

### Parameters
#### Request Body
- **data_path** (str) - Optional - Path to a folder containing image files.
- **hf_dataset** (Dataset) - Optional - A Hugging Face dataset object.
- **image_key** (str) - Optional - The key for the image feature in the Hugging Face dataset.
- **torchvision_dataset** (Dataset) - Optional - A Torchvision dataset object.

### Request Example
from cleanvision import Imagelab
imagelab = Imagelab(data_path="FOLDER_WITH_IMAGES/")

## find_issues()

### Description
Analyzes the dataset to find predefined or specific image quality issues.

### Parameters
#### Request Body
- **issue_types** (dict) - Optional - A dictionary specifying which issues to detect (e.g., {"light": {}, "blurry": {}}).

### Request Example
imagelab.find_issues()

## report()

### Description
Generates a summary report of the issues detected in the dataset.

### Parameters
#### Request Body
- **issue_types** (list) - Optional - A list of specific issue types to include in the report.

### Response
#### Success Response (200)
- **report** (object) - A summary of detected issues per image.
```

--------------------------------

### Access Image Issue Statistics Summary

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Explains how to access the 'issue_summary' attribute, a DataFrame providing aggregated statistics on the number of images affected by each issue type. Examples include calculating total issues, finding the most common issue, and filtering for significant issues.

```python
from cleanvision import Imagelab

imagelab = Imagelab(data_path="/path/to/images/")
imagelab.find_issues()

# Access the issue summary DataFrame
summary = imagelab.issue_summary
print(summary)

# Get total number of issues found
total_issues = summary["num_images"].sum()

# Get most common issue
most_common = summary.iloc[0]["issue_type"]

# Filter to issues affecting more than 5 images
significant_issues = summary[summary["num_images"] > 5]
```

--------------------------------

### Access and Filter Image Issues DataFrame

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Shows how to access the 'issues' attribute, which is a pandas DataFrame containing quality scores and issue flags for each image. It includes examples of filtering images based on issue types, sorting by scores, and counting multiple issues per image.

```python
from cleanvision import Imagelab

imagelab = Imagelab(data_path="/path/to/images/")
imagelab.find_issues()

# Access the issues DataFrame
df = imagelab.issues
print(df.head())

# Get all images flagged with dark issue
dark_images = df[df["is_dark_issue"] == True]

# Get images sorted by blurriness (most blurry first)
blurry_sorted = df.sort_values("blurry_score", ascending=True)

# Get images with multiple issues
df["issue_count"] = (
    df["is_dark_issue"].astype(int) +
    df["is_blurry_issue"].astype(int) +
    df["is_low_information_issue"].astype(int)
)
multi_issue_images = df[df["issue_count"] > 1]

# Filter by score threshold
severely_dark = df[df["dark_score"] < 0.1]
```

--------------------------------

### Integrate CleanVision with Torchvision Datasets

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Shows how to combine Torchvision datasets and process them using Imagelab, including recommended settings for parallel execution.

```python
from torchvision.datasets import CIFAR10
from torch.utils.data import ConcatDataset
from cleanvision import Imagelab

train_set = CIFAR10(root="./data", train=True, download=True)
test_set = CIFAR10(root="./data", train=False, download=True)
dataset = ConcatDataset([train_set, test_set])
imagelab = Imagelab(torchvision_dataset=dataset)
imagelab.find_issues(n_jobs=1)
imagelab.report()
```

--------------------------------

### Detect and Report Image Dataset Issues

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/tutorial.ipynb

Initialize the Imagelab class to scan a directory of images for quality issues and generate a summary report.

```python
from cleanvision import Imagelab

dataset_path = "./image_files/"
imagelab = Imagelab(data_path=dataset_path)
imagelab.find_issues()
imagelab.report()
```

--------------------------------

### Detect Specific Issues Incrementally

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/tutorial.ipynb

Initializes an Imagelab instance to find specific issues and demonstrates how to incrementally add more checks to the same instance.

```python
imagelab = Imagelab(data_path=dataset_path)
issue_types = {"dark": {}}
imagelab.find_issues(issue_types)
imagelab.report()

# Add another check
issue_types = {"blurry": {}}
imagelab.find_issues(issue_types)
imagelab.report()
```

--------------------------------

### Audit Image Dataset with Imagelab

Source: https://github.com/cleanlab/cleanvision/blob/main/README.md

Initializes Imagelab with the path to your image dataset and runs an audit to find common image issues. It then generates a report detailing the findings. This is the core functionality for dataset analysis.

```python
from cleanvision import Imagelab

# Specify path to folder containing the image files in your dataset
imagelab = Imagelab(data_path="FOLDER_WITH_IMAGES/")

# Automatically check for a predefined list of issues within your dataset
imagelab.find_issues()

# Produce a neat report of the issues found in your dataset
imagelab.report()
```

--------------------------------

### Audit Dataset with Imagelab

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/torchvision_dataset.ipynb

Initializes the Imagelab class with the dataset and executes the issue detection process. This identifies potential problems across the image collection.

```python
from cleanvision import Imagelab

imagelab = Imagelab(torchvision_dataset=dataset)
imagelab.find_issues()
```

--------------------------------

### Configure Cloud Storage for Image Datasets

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Explains how to connect Imagelab to various cloud storage providers like AWS S3, Google Cloud Storage, and Azure Blob Storage using fsspec.

```python
from cleanvision import Imagelab

# AWS S3
imagelab_s3 = Imagelab(data_path="s3://bucket-name/images/", storage_opts={"key": "AWS_ACCESS_KEY", "secret": "AWS_SECRET_KEY"})

# Google Cloud Storage
imagelab_gcs = Imagelab(data_path="gs://bucket-name/images/", storage_opts={"token": "/path/to/credentials.json"})

# Azure Blob Storage
imagelab_az = Imagelab(data_path="az://container-name/images/", storage_opts={"account_name": "...", "account_key": "..."})
```

--------------------------------

### Visualize Image Issues with Imagelab

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Demonstrates how to visualize specific types of image issues like 'dark' or 'blurry' using the imagelab.visualize method. Customization options include the number of images to display, cell size, and whether to show image IDs.

```python
from cleanvision import Imagelab

imagelab = Imagelab(data_path="/path/to/images/")

# Visualize top examples of specific issue types
imagelab.visualize(issue_types=["dark", "blurry"])
imagelab.visualize(issue_types=["near_duplicates"], num_images=4)

# Customize image display size
imagelab.visualize(cell_size=(3, 3))

# Show image IDs in visualization
imagelab.visualize(show_id=True)
```

--------------------------------

### Access Detailed Image Statistics with Imagelab.info

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Details how to access the 'info' dictionary, which contains detailed statistics and metadata computed during issue detection. This includes image properties like brightness, entropy, aspect ratios, and information about duplicate image sets.

```python
from cleanvision import Imagelab

imagelab = Imagelab(data_path="/path/to/images/")
imagelab.find_issues()

# Access computed statistics
stats = imagelab.info["statistics"]
print(stats.keys())

# Get brightness statistics across dataset
brightness_stats = stats["brightness"]
print(brightness_stats)

# Access duplicate sets (groups of duplicate images)
exact_dup_sets = imagelab.info["exact_duplicates"]["sets"]
near_dup_sets = imagelab.info["near_duplicates"]["sets"]

# Each set is a list of image indices that are duplicates of each other
for dup_set in exact_dup_sets:
    print(f"Duplicate group: {dup_set}")
```

--------------------------------

### Visualize Image Issues

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/tutorial.ipynb

Shows how to visualize specific images identified as having issues, either by passing file lists or specifying issue types directly.

```python
imagelab.visualize(image_files=blurry_image_files[:4])
imagelab.visualize(issue_types=["blurry"])
```

--------------------------------

### Imagelab Module Overview

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/cleanvision/imagelab.rst

This section provides an overview of the Imagelab module, including its members, undocumented members, and inheritance information.

```APIDOC
## Imagelab Module

### Description
The `cleanvision.imagelab` module provides functionalities for image analysis and labeling. It includes various methods for processing and understanding image datasets.

### Module Details
This documentation is generated using `automodule` and includes:

- **autosummary**: Generates a summary table of the module's contents.
- **members**: Lists all public members (classes, functions, variables) of the module.
- **undoc-members**: Includes members that are not explicitly documented.
- **show-inheritance**: Displays the inheritance hierarchy for classes within the module.

### Usage
To use the Imagelab module, import it into your Python environment:

```python
import cleanvision.imagelab
```

Refer to the specific functions and classes within the module for detailed usage instructions.
```

--------------------------------

### Retrieve Computed Statistics with Imagelab.get_stats()

Source: https://context7.com/cleanlab/cleanvision/llms.txt

Demonstrates the use of the `get_stats()` convenience method to retrieve the dictionary of computed statistics. It shows how to access specific properties like mean brightness, standard deviation, mean aspect ratio, and mean entropy.

```python
from cleanvision import Imagelab

imagelab = Imagelab(data_path="/path/to/images/")
imagelab.find_issues()

# Get all computed statistics
stats = imagelab.get_stats()

# Access specific property statistics
if "brightness" in stats:
    print(f"Mean brightness: {stats['brightness']['mean']:.3f}")
    print(f"Brightness std: {stats['brightness']['std']:.3f}")

if "aspect_ratio" in stats:
    print(f"Mean aspect ratio: {stats['aspect_ratio']['mean']:.3f}")

if "entropy" in stats:
    print(f"Mean entropy: {stats['entropy']['mean']:.3f}")
```

--------------------------------

### Viz Manager Utility

Source: https://github.com/cleanlab/cleanvision/blob/main/docs/source/cleanvision/utils/viz_manager.rst

Details about the Viz Manager utility, including its members and inheritance hierarchy.

```APIDOC
## Viz Manager

### Description
The Viz Manager is a utility module within the cleanvision.utils package designed for visualization purposes. This documentation outlines its members and inheritance.

### Module
`cleanvision.utils.viz_manager`

### Members
- `__init__`
- `__module__`
- `__qualname__`
- `get_cleanlab_viz`
- `get_cleanlab_viz_params`
- `get_cleanlab_viz_params_dict`
- `get_cleanlab_viz_params_list`
- `get_cleanlab_viz_params_str`
- `get_cleanlab_viz_params_tuple`
- `get_cleanlab_viz_params_tuple_list`
- `get_cleanlab_viz_params_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
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- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
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- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
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- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_list`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_str`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple`
- `get_cleanlab_viz_params_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple_tuple
```