### Halide Basics Tutorial Example

Source: https://halide-lang.org/docs/tutorial_2lesson_03_debugging_1_8cpp-example

Illustrates the fundamental concepts of the Halide language, including defining functions, stages, and basic image manipulation. This is typically the starting point for learning Halide.

```cpp
#include "Halide.h"

// Example Halide function definition
int main(int argc, char **argv) {
    Halide::Buffer<uint8_t> input = ...;
    Halide::Var x, y, c;

    // Define a simple Halide function
    Halide::Func brighter;
    brighter(x, y, c) = Halide::cast<uint8_t>(Halide::min(255, input(x, y, c) * 2));

    // ... (further code for compilation and execution)
    return 0;
}
```

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

### Using Halide from CMake Example

Source: https://halide-lang.org/docs/classcmdline_1_1detail_1_1lexical__cast__t_3_01_target_00_01_source_00_01true_01_4-members

Shows how to use an installed Halide library within a CMake project. This snippet focuses on finding the Halide installation and linking your executable against it.

```cmake
# CMakeLists.txt to use an installed Halide
cmake_minimum_required(VERSION 3.10)
project(MyHalideApp)

# Find the Halide package, assuming it's installed
# You might need to set CMAKE_PREFIX_PATH to your Halide installation directory
find_package(Halide REQUIRED)

# Create your application executable
add_executable(my_app main.cpp)

# Link your application against the Halide library
target_link_libraries(my_app PRIVATE Halide::Halide)

# Ensure Halide headers are included
target_include_directories(my_app PRIVATE ${Halide_INCLUDE_DIRS})

```

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

### Halide Examples

Source: https://halide-lang.org/docs/structcmdline_1_1detail_1_1is__same_3_01_t_00_01_t_01_4-members

This section provides access to various examples demonstrating Halide usage, including building, contributing, testing, and platform-specific applications.

```APIDOC
## Halide Examples

### Description
Provides access to various examples demonstrating Halide usage, including building, contributing, testing, and platform-specific applications.

### Method
N/A

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A
```

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

### Basic Halide Tutorial Example

Source: https://halide-lang.org/docs/tutorial_2lesson_10_aot_compilation_generate_8cpp-example

Demonstrates the fundamental usage of the Halide API for image processing. This example covers defining a simple image pipeline.

```cpp
#include "Halide.h"
#include <iostream>

int main(int argc, char **argv) {
    Halide::Buffer<uint8_t> input = Halide::Tools::load_image("input.png");

    // Define a Halide pipeline
    Halide::Var x, y;
    Halide::Func brighter;
    brighter(x, y) = Halide::cast<uint8_t>(input(x, y) * 1.2f);

    // Schedule and realize the pipeline
    brighter.vectorize(x, 8).parallel(y);
    Halide::Buffer<uint8_t> output = brighter.realize({input.width(), input.height()});

    // Save the output image
    Halide::Tools::save_image(output, "output.png");

    std::cout << "Success!" << std::endl;
    return 0;
}
```

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

### Halide Func Realization with Examples

Source: https://halide-lang.org/docs/_func_8h_source

Shows how to evaluate a Halide Func over a domain and return the result as a buffer. Includes examples for single-value and multi-value functions, and how to pass Param and ImageParam arguments.

```cpp
/** Evaluate this function over some rectangular domain and return the resulting buffer or buffers. ... */
Realization realize(std::vector<int32_t> sizes = {}, const Target &target = Target());

/** Same as above, but takes a custom user-provided context to be passed to runtime functions. ... */
Realization realize(JITUserContext *context,
std::vector<int32_t> sizes = {},
const Target &target = Target());

/** Evaluate this function into an existing allocated buffer or buffers. ... */
void realize(const std::vector<Buffer<>> &buffers, ...);

// Example: Single value realization
f(x) = sin(x);
Buffer<float> im = f.realize(...);

// Example: Multi-value realization
f(x) = Tuple(x, sin(x));
Realization r = f.realize(...);
Buffer<int> im0 = r[0];
Buffer<float> im1 = r[1];

// Example: Using Param and ImageParam
Param<int32> p(42);
ImageParam img(Int(32), 1);
f(x) = img(x) + p;

Buffer<int32_t> arg_img(10, 10);
// <fill in arg_img...>

Target t = get_jit_target_from_environment();
Buffer<int32_t> result = f.realize({10, 10}, t);

// Example: Using initializer list for arguments
Buffer<int32_t> result = f.realize({10, 10}, t, { { p, 17 }, { img, arg_img } });
```

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

### Halide Examples

Source: https://halide-lang.org/docs/namespace_halide_1_1_runtime_1_1_internal_1_1_qurt

Indicates the presence of example code or usage demonstrations for Halide.

```Halide
►Examples
```

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

### Halide Examples and Usage

Source: https://halide-lang.org/docs/structcmdline_1_1detail_1_1is__same-members

Documentation on how to use Halide through various examples, including build system integration, specific hardware backends, and language bindings.

```APIDOC
## Halide Examples and Guides

### Description
This section outlines various examples and guides for using Halide, covering build system integration, targeting specific hardware, and leveraging language bindings.

### Method
N/A (Documentation Outline)

### Endpoint
N/A

### Examples

#### Building Halide with CMake

##### Description
Instructions on how to build the Halide compiler and libraries using the CMake build system.

##### Method
N/A

##### Endpoint
N/A

#### Contributing CMake code to Halide

##### Description
Guidelines for developers contributing CMake-related code to the Halide project.

##### Method
N/A

##### Endpoint
N/A

#### Fuzz testing

##### Description
Information and methods for performing fuzz testing on Halide to discover potential bugs and vulnerabilities.

##### Method
N/A

##### Endpoint
N/A

#### Using Halide from your CMake build

##### Description
Instructions on how to integrate Halide into your own CMake-based projects.

##### Method
N/A

##### Endpoint
N/A

#### Halide for Hexagon HVX

##### Description
Specific guidance and examples for utilizing Halide with Hexagon Heterogeneous Compute (HVX) processors.

##### Method
N/A

##### Endpoint
N/A

#### Halide Bindings for Python

##### Description
Documentation on how to use Halide from Python, including installation and usage of Python bindings.

##### Method
N/A

##### Endpoint
N/A

#### Running and Benchmarking Halide Generators

##### Description
Procedures for executing Halide generators and measuring their performance.

##### Method
N/A

##### Endpoint
N/A

#### Testing

##### Description
Overview of the testing framework and procedures for Halide.

##### Method
N/A

##### Endpoint
N/A

#### Vulkan Support for Halide

##### Description
Information on using Halide with the Vulkan graphics API for GPU acceleration.

##### Method
N/A

##### Endpoint
N/A

#### WebAssembly Support for Halide

##### Description
Details on compiling and running Halide code in WebAssembly environments.

##### Method
N/A

##### Endpoint
N/A

#### WebGPU support for Halide

##### Description
Information regarding Halide's support for the WebGPU API for modern graphics and compute.

##### Method
N/A

##### Endpoint
N/A

#### Topics

##### Description
Links to various topics and conceptual explanations related to Halide.

##### Method
N/A

##### Endpoint
N/A

#### Namespaces

##### Description
Reference to Halide's namespaces for organizing code elements.

##### Method
N/A

##### Endpoint
N/A

#### Classes

##### Description
Detailed documentation for Halide's classes.

##### Method
N/A

##### Endpoint
N/A

###### Class List

####### Description
A list of all classes available in the Halide API.

####### Method
N/A

####### Endpoint
N/A

###### Class: cmdline


####### Description
Provides command-line argument parsing capabilities.

####### Method
N/A

####### Endpoint
N/A

####### Nested Namespace: detail


























































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































```

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

### Install CMake on Windows using winget

Source: https://halide-lang.org/docs/md_doc_2_building_halide_with_c_make

Installs CMake on Windows using the Windows Package Manager (winget). This is a convenient command-line method for installing applications on Windows.

```bash
winget install Kitware.CMake
```

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

### Install Ninja using winget

Source: https://halide-lang.org/docs/md_doc_2_building_halide_with_c_make

Installs the Ninja build system on Windows using the Windows Package Manager (winget).

```bash
winget install Ninja-build.Ninja
```

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

### Halide compute_with example

Source: https://halide-lang.org/docs/_func_8h_source

Shows how to fuse stages using compute_with, explaining the parent-child relationship for fused computations and illustrating with code examples.

```Halide
input(x, y) = x + y;
f(x, y) = input(x, y);
f(x, y) += 5;
g(x, y) = x - y;
g(x, y) += 10;
f.compute_with(g, y);
f.update().compute_with(g.update(), y);

```

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

### Install Ninja using APT

Source: https://halide-lang.org/docs/md_doc_2_building_halide_with_c_make

Installs the Ninja build system on Ubuntu/Debian Linux using the APT package manager.

```bash
apt install ninja-build
```

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

### Install Halide with CMake (Multi-Config)

Source: https://halide-lang.org/docs/md_doc_2_building_halide_with_c_make

Installs Halide using CMake for multi-configuration generators like Visual Studio. It requires specifying the configuration (e.g., Release) and an installation prefix.

```bash
cmake --install ./build --prefix /path/to/Halide-install --config Release
```

```bash
cmake --install .\build --prefix X:\path\to\Halide-install --config Release
```

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

### Halide Input Image Tutorial Example

Source: https://halide-lang.org/docs/tutorial_2lesson_03_debugging_1_8cpp-example

Shows how to load and use an input image within a Halide pipeline. This example covers the process of making external image data accessible to Halide functions.

```cpp
#include "Halide.h"
#include <iostream>
#include <vector>

// Example of loading an input image into a Halide buffer
int main(int argc, char **argv) {
    // Assuming 'load_image' function exists and returns a Halide::Buffer
    Halide::Buffer<uint8_t> input = load_image("path/to/your/image.png"); 

    Halide::Var x, y, c;
    Halide::Func f;
    f(x, y, c) = input(x, y, c);

    // ... (further code for compilation and execution)
    return 0;
}
```

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

### Install Ninja using Homebrew

Source: https://halide-lang.org/docs/md_doc_2_building_halide_with_c_make

Installs the Ninja build system on macOS using the Homebrew package manager.

```bash
brew install ninja
```

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

### Get JIT Externs

Source: https://halide-lang.org/docs/class_halide_1_1_pipeline

Returns the map of previously installed externs.

```APIDOC
## GET /get_jit_externs

### Description
Returns the map of previously installed externs. It is an empty map unless set otherwise.

### Method
GET

### Endpoint
/get_jit_externs

### Parameters
None

### Response
#### Success Response (200)
- **externs** (const std::map< std::string, JITExtern > &) - The map of installed externs.

#### Response Example
```json
{
  "externs": {
    "my_external_func": "..."
  }
}
```
```

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

### Clone and Bootstrap Dawn Repository

Source: https://halide-lang.org/docs/md_doc_2_web_g_p_u

Instructions for cloning the Dawn repository and setting up the gclient configuration to include Node.js bindings, a prerequisite for building Dawn's Node.js components.

```bash
# Clone the Dawn repository
git clone https://dawn.googlesource.com/dawn
cd dawn

# Copy the gclient configuration for Node.js bindings
cp scripts/standalone-with-node.gclient .gclient

# Fetch dependencies and toolchains
gclient sync
```

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

### Get JIT Externs

Source: https://halide-lang.org/docs/_pipeline_8h_source

Retrieves the map of previously installed externs used by JIT compilation.

```APIDOC
## const std::map<std::string, JITExtern> &get_jit_externs()

### Description
Return the map of previously installed externs. Is an empty map unless set otherwise.

### Method
GET

### Endpoint
/websites/halide-lang

### Response
#### Success Response (200)
- **externs** (std::map<std::string, JITExtern>) - The map of installed JIT externs.

#### Response Example
```json
{
  "externs": {
    "my_external_func": {
      "ptr": "0x12345678",
      "call_conv": "halide_default_call_extern"
    }
  }
}
```
```

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

### Get JIT Externs

Source: https://halide-lang.org/docs/class_halide_1_1_pipeline

Returns the map of previously installed externs. Returns an empty map if none have been set.

```c++
const std::map<std::string, JITExtern>& Halide::Pipeline::get_jit_externs()
```

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

### Halide Tutorial: Lesson 01 - Basics (C++)

Source: https://halide-lang.org/docs/tutorial_2lesson_01_basics_8cpp-example

Introduces the fundamental concepts of Halide, demonstrating how to define and compile a simple image processing pipeline. This is the first step in learning Halide.

```cpp
#include "Halide.h"
#include <iostream>

int main(int argc, char **argv) {
    // Declare a Halide Func
    Halide::Func brighter;

    // Declare Halide Var
    Halide::Var x, y;

    // Define the Func
    // brighter(x, y) = a_pixel(x, y) * 2.0f;
    brighter(x, y) = Halide::cast<uint8_t>(Halide::min(255, Halide::cast<int>(Halide::loadImage("images/small_gray.png").rgb_to_grayscale(x, y)) * 2));

    // Define the output. 
    // brighter.output_buffer(x, y, Halide::UInt(8));

    // Visualize the output
    brighter.realize(100, 100);

    // Save the output
    Halide::save_image(brighter.realize({100, 100}), "brighter.png");

    std::cout << "Success!" << std::endl;
    return 0;
}
```

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

### Halide Python Bindings Example

Source: https://halide-lang.org/docs/tutorial_2lesson_11_cross_compilation_8cpp-example

Demonstrates how to use Halide's Python bindings for image processing tasks. This example shows basic image loading and manipulation using Halide within a Python environment.

```python
import halide

# Assuming a Halide function 'im' is defined and compiled
# For example:
# x, y, c = halide.expr.var('x'), halide.expr.var('y'), halide.expr.var('c')
# input_image = halide.Image<uint8_t>(100, 100, 3)
# im = halide.compute_relu(input_image[x, y, c])

# Load an input image (replace with actual image loading logic)
# input_buffer = halide.Buffer<uint8_t>(width, height, channels)
# halide.load_image("path/to/your/image.png", input_buffer)

# Define a simple Halide pipeline (example: identity function)
# x, y, c = halide.expr.var('x'), halide.expr.var('y'), halide.expr.var('c')
# input = halide.ImageParam(halide.UInt(8), 3, "input")
# output = halide.compute_relu(input[x, y, c])
# pipeline = halide.Pipeline(output)

# Compile the pipeline (if not already compiled)
# compiled_pipeline = pipeline.compile()

# Create an output buffer
# output_buffer = halide.Buffer<uint8_t>(width, height, channels)

# Run the pipeline
# compiled_pipeline.realize(output_buffer)

# Save or process the output image
# halide.save_image(output_buffer, "output.png")

print("Halide Python bindings example executed.")
```

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

### Get JIT External Functions

Source: https://halide-lang.org/docs/_pipeline_8h_source

Retrieves the map of previously installed externs. Returns an empty map if no externs have been set.

```cpp
/** Return the map of previously installed externs. Is an empty
 * map unless set otherwise. */
const std::map<std::string, JITExtern> &get_jit_externs();
```

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

### Halide Basics Tutorial (C++)

Source: https://halide-lang.org/docs/tutorial_2lesson_10_aot_compilation_run_8cpp-example

Demonstrates the fundamental usage of the Halide API, including defining functions and stages. This is the first lesson in the Halide tutorial series.

```C++
#include "Halide.h"
#include <iostream>

int main(int argc, char **argv) {
    Halide::Buffer<int32_t> input = Halide::Buffer<int32_t>::make_interleaved(100, 100, 3);

    // Fill the buffer with some values
    for (int y = 0; y < 100; y++) {
        for (int x = 0; x < 100; x++) {
            for (int c = 0; c < 3; c++) {
                input(x, y, c) = x + y + c;
            }
        }
    }

    Halide::Var x, y, c;
    Halide::Func brighten;

    brighten(x, y, c) = input(x, y, c) + 100;

    // The schedule can be very simple for simple functions.
    // We can just call realize directly.
    Halide::Buffer<int32_t> output = brighten.realize(100, 100, 3);

    // Verify the output (optional)
    for (int y = 0; y < 100; y++) {
        for (int x = 0; x < 100; x++) {
            for (int c = 0; c < 3; c++) {
                if (output(x, y, c) != (x + y + c + 100)) {
                    std::cerr << "Error!" << std::endl;
                    return -1;
                }
            }
        }
    }

    std::cout << "Success!" << std::endl;
    return 0;
}
```

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

### Manage JIT Externs (C++)

Source: https://halide-lang.org/docs/_pipeline_8h_source

Provides methods to get and set external C functions or Funcs for JIT compilation. 'get_jit_externs' returns a map of installed externs, while 'set_jit_externs' installs a new set. Dependencies include Halide's std::map, JITExtern, and std::string.

```c++
const std::map< std::string, JITExtern > & get_jit_externs()
void set_jit_externs(const std::map< std::string, JITExtern > &externs)
```

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

### Get Vulkan Layer Names (C)

Source: https://halide-lang.org/docs/_halide_runtime_vulkan_8h

Retrieves the currently set Vulkan layer names. This is useful for debugging or introspection of the Vulkan setup.

```c
const char * halide_vulkan_get_layer_names(void *_user_context)
```

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

### Basic Halide Tutorial (C++)

Source: https://halide-lang.org/docs/tutorial_2lesson_09_update_definitions_8cpp-example

Demonstrates the fundamental usage of the Halide language, likely involving defining and running simple image processing pipelines. This serves as an entry point for new users to understand Halide's core concepts.

```cpp
// tutorial/lesson_01_basics.cpp
// This file is a placeholder and does not contain actual code.
```

```cpp
// tutorial/lesson_02_input_image.cpp
// This file is a placeholder and does not contain actual code.
```

```cpp
// tutorial/lesson_03_debugging_1.cpp
// This file is a placeholder and does not contain actual code.
```

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

### Install Doxygen on Windows using winget

Source: https://halide-lang.org/docs/md_doc_2_building_halide_with_c_make

Installs the Doxygen documentation system on Windows using the Windows Package Manager (winget). This is required for building Halide's documentation.

```bash
winget install DimitriVanHeesch.Doxygen
```

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

### Get Iterator to Start of Buffer Dimension in Halide

Source: https://halide-lang.org/docs/class_halide_1_1_runtime_1_1_buffer_1_1_dimension

Provides an iterator pointing to the minimum coordinate of a buffer dimension. This is fundamental for iterating over buffer elements.

```c++
template<typename T = void, int Dims = AnyDims, int InClassDimStorage = (Dims == AnyDims ? 4 : std::max(Dims, 1))>
inline iterator Halide::Runtime::Buffer< T, Dims, InClassDimStorage >::Dimension::begin() const
```

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

### Halide Vulkan Support

Source: https://halide-lang.org/docs/classcmdline_1_1detail_1_1lexical__cast__t_3_01_target_00_01_source_00_01true_01_4

Details on how to leverage Vulkan for GPU acceleration with Halide. This section likely covers setup and examples of using Vulkan pipelines within Halide.

```c++
// C++ code related to Halide Vulkan support

```

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

### Halide Basics Tutorial (C++)

Source: https://halide-lang.org/docs/tutorial_2lesson_11_cross_compilation_8cpp-example

Illustrates the fundamental concepts of the Halide programming language using C++. This example covers defining simple image processing functions and understanding Halide's expression syntax.

```cpp
#include <Halide.h>
#include <iostream>

int main(int argc, char **argv) {
    Halide::Buffer<uint8_t> input = Halide::create_buffer<uint8_t>(100, 100, 3);
    // Fill input buffer with some data (e.g., a gradient)
    for (int y = 0; y < input.height(); ++y) {
        for (int x = 0; x < input.width(); ++x) {
            for (int c = 0; c < input.channels(); ++c) {
                input(x, y, c) = (x + y + c * 50) % 256;
            }
        }
    }

    Halide::Var x, y, c;
    Halide::Func brighter;

    // Define the Halide 'brighter' function
    // For each pixel (x, y, c), add 50 to its value, clamping at 255
    brighter(x, y, c) = Halide::min(input(x, y, c) + 50, 255);

    // Describe how to process the image on different backends
    // For CPU, we can simply realize it directly
    brighter.vectorize(x, 8).parallel(y);

    // Realize the function into an output buffer
    Halide::Buffer<uint8_t> output = brighter.realize(input.width(), input.height(), input.channels());

    // You can now inspect or save the 'output' buffer
    std::cout << "Halide basics tutorial executed. Output buffer created." << std::endl;

    return 0;
}

```

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

### Get Slice Beginning Index

Source: https://halide-lang.org/docs/struct_halide_1_1_internal_1_1_shuffle

Returns the starting index of a slice within the vector arguments. This is used in conjunction with `is_slice` to define the slice boundaries.

```cpp
int Halide::Internal::Shuffle::slice_begin() const
```

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

### Instance and Adapter Management

Source: https://halide-lang.org/docs/globals_w

Functions for managing WebGPU instances and adapters, including requesting adapters and enumerating features.

```APIDOC
## wgpuInstanceRequestAdapterF()

### Description
Requests an adapter from the WebGPU instance.

### Method
GET

### Endpoint
/webgpu/instance/requestAdapterF

### Parameters
#### Query Parameters
- **instance** (WGPUInstance) - Required - The WebGPU instance.
- **options** (WGPURequestAdapterOptions) - Optional - Options for requesting the adapter.

### Response
#### Success Response (200)
- **adapter** (WGPUAdapter) - The requested adapter.

#### Response Example
```json
{
  "adapter": "<WGPUAdapter object>"
}
```
```

```APIDOC
## wgpuInstanceWaitAny()

### Description
Waits for any of the given instances to become ready.

### Method
GET

### Endpoint
/webgpu/instance/waitAny

### Parameters
#### Query Parameters
- **instances** ([WGPUInstance]) - Required - A list of WebGPU instances.

### Response
#### Success Response (200)
- **readyInstances** ([WGPUInstance]) - A list of instances that are ready.

#### Response Example
```json
{
  "readyInstances": [
    "<WGPUInstance object 1>",
    "<WGPUInstance object 2>"
  ]
}
```
```

```APIDOC
## WGPULoadOp Enumeration

### Description
Defines the load operations for render targets.

### Values
- **WGPULoadOp_Clear**: Clear the target.
- **WGPULoadOp_Load**: Load the existing content.
- **WGPULoadOp_Undefined**: Undefined load operation.
- **WGPULoadOp_Force32**: Force 32-bit size.
```

```APIDOC
## WGPULoggingCallback and WGPULoggingType

### Description
Callback function and type for logging messages.

### Values (WGPULoggingType)
- **WGPULoggingType_Verbose**: Verbose logging.
- **WGPULoggingType_Info**: Informational logging.
- **WGPULoggingType_Warning**: Warning logging.
- **WGPULoggingType_Error**: Error logging.
- **WGPULoggingType_Force32**: Force 32-bit size.
```

```APIDOC
## WGPUMapMode Enumeration

### Description
Defines the mapping modes for buffers.

### Values
- **WGPUMapMode_None**: No mapping.
- **WGPUMapMode_Read**: Read mapping.
- **WGPUMapMode_Write**: Write mapping.
- **WGPUMapMode_Force32**: Force 32-bit size.
```

```APIDOC
## WGPUMipmapFilterMode Enumeration

### Description
Defines the mipmap filter modes.

### Values
- **WGPUMipmapFilterMode_Nearest**: Nearest mipmap filter.
- **WGPUMipmapFilterMode_Linear**: Linear mipmap filter.
- **WGPUMipmapFilterMode_Undefined**: Undefined mipmap filter mode.
- **WGPUMipmapFilterMode_Force32**: Force 32-bit size.
```

```APIDOC
## wgpuPipelineLayoutReference() and wgpuPipelineLayoutRelease()

### Description
Reference and release operations for pipeline layouts.

### Method
POST

### Endpoint
/webgpu/pipeline-layout/{operation}

### Parameters
#### Path Parameters
- **operation** (string) - Required - 'reference' or 'release'.

#### Request Body
- **pipelineLayout** (WGPUPipelineLayout) - Required - The pipeline layout object.
```

```APIDOC
## wgpuPipelineLayoutSetLabel()

### Description
Sets a label for the pipeline layout.

### Method
POST

### Endpoint
/webgpu/pipeline-layout/setLabel

### Parameters
#### Request Body
- **pipelineLayout** (WGPUPipelineLayout) - Required - The pipeline layout object.
- **label** (string) - Required - The label to set.
```

```APIDOC
## WGPUPowerPreference Enumeration

### Description
Defines the power preference for adapter selection.

### Values
- **WGPUPowerPreference_LowPower**: Prefer low power.
- **WGPUPowerPreference_HighPerformance**: Prefer high performance.
- **WGPUPowerPreference_Undefined**: Undefined power preference.
- **WGPUPowerPreference_Force32**: Force 32-bit size.
```

```APIDOC
## WGPUPresentMode Enumeration

### Description
Defines the present modes for swap chains.

### Values
- **WGPUPresentMode_Immediate**: Immediate presentation.
- **WGPUPresentMode_Mailbox**: Mailbox presentation.
- **WGPUPresentMode_Fifo**: FIFO presentation.
- **WGPUPresentMode_Force32**: Force 32-bit size.
```

```APIDOC
## WGPUPrimitiveTopology Enumeration

### Description
Defines the primitive topologies for rendering.

### Values
- **WGPUPrimitiveTopology_PointList**: Point list.
- **WGPUPrimitiveTopology_LineList**: Line list.
- **WGPUPrimitiveTopology_LineStrip**: Line strip.
- **WGPUPrimitiveTopology_TriangleList**: Triangle list.
- **WGPUPrimitiveTopology_TriangleStrip**: Triangle strip.
- **WGPUPrimitiveTopology_Undefined**: Undefined primitive topology.
- **WGPUPrimitiveTopology_Force32**: Force 32-bit size.
```

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

### Get Block and Serial Extents

Source: https://halide-lang.org/docs/struct_halide_1_1_internal_1_1_autoscheduler_1_1_loop_nest

Calculates the start and end extents for a specific block within a serial loop. This method is important for understanding the bounds of computations.

```cpp
std::pair< int64_t, int64_t > Halide::Internal::Autoscheduler::LoopNest::get_block_and_serial_extents(const LoopNest * _block_) const
```

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

### Get Default Device API for Target (C++)

Source: https://halide-lang.org/docs/_device_interface_8h

Determines the default DeviceAPI that Halide selects for a given target when DeviceAPI::Default_GPU is specified. This is crucial for GPU acceleration setup.

```cpp
DeviceAPI Halide::get_default_device_api_for_target(const Target &t)
```

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

### Halide Input Image Tutorial (C++)

Source: https://halide-lang.org/docs/examples

Demonstrates how to load an input image into a Halide pipeline for processing. Covers defining input placeholders and loading image data.

```cpp
#include "Halide.h"
#include <iostream>

int main(int argc, char **argv) {
    Halide::Buffer<uint8_t> input = Halide::load_image("input.png");

    Halide::Var x, y;
    Halide::Func brighter;

    brighter(x, y) = Halide::cast<uint8_t>(Halide::min(255, Halide::cast<int>(input(x, y)) + 50));

    brighter.realize("output.png");

    std::cout << "Successfully generated brighter image!" << std::endl;
    return 0;
}
```

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

### Halide GPUCompilationCache kernel_state_setup() Function

Source: https://halide-lang.org/docs/class_halide_1_1_internal_1_1_g_p_u_compilation_cache

The kernel_state_setup function prepares the state for a GPU kernel compilation. It takes a user context, pointers for state setup, the context, the result module state, the compilation function, and any necessary arguments.

```cpp
template<typename ContextT , typename ModuleStateT >
template<typename CompileModuleT , typename... Args>
HALIDE_MUST_USE_RESULT bool Halide::Internal::GPUCompilationCache< ContextT, ModuleStateT >::kernel_state_setup  ( void * _user_context_,
void ** _state_ptr_ptr_,
ContextT _context_,
ModuleStateT & _result_,
CompileModuleT _f_,
Args... _args_ )
```

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

### Halide Basics Tutorial (C++)

Source: https://halide-lang.org/docs/examples

Introduction to Halide, covering fundamental concepts and syntax for defining image processing pipelines. Demonstrates basic function definition and usage.

```cpp
#include "Halide.h"
#include <iostream>

int main(int argc, char **argv) {
    // Define Halide variables
    Halide::Var x, y;

    // Define a Halide function representing an image
    Halide::Func brighter;

    // Define the computation for the function
    // Here, we're simply adding 50 to each pixel value.
    brighter(x, y) = Halide::cast<uint8_t>(Halide::min(255, Halide::cast<int>(Halide::load_input("input"))(x, y) + 50));

    // Realize the function to produce an output image.
    // This step compiles and executes the Halide pipeline.
    brighter.realize("output.png");

    std::cout << "Successfully generated brighter image!" << std::endl;
    return 0;
}
```

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

### Get Vectorized Loop Index from Pure Stage

Source: https://halide-lang.org/docs/struct_halide_1_1_internal_1_1_autoscheduler_1_1_loop_nest

Retrieves the vectorized loop index starting from a pure stage within the LoopNest. This function works from the root of the loop nest.

```c++
int Halide::Internal::Autoscheduler::LoopNest::get_vectorized_loop_index_from_pure_stage(const LoopNest& _root_) const
```

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

### Halide Input Image Tutorial (C++)

Source: https://halide-lang.org/docs/tutorial_2lesson_12_using_the_gpu_8cpp-example

Shows how to load and process an input image using Halide. This example focuses on handling image data as input to a Halide pipeline.

```cpp
#include "Halide.h"
#include "HalideBuffer.h"
#include "tools/halide_image_io.h"

int main(int argc, char **argv) {
    Halide::Buffer<uint8_t> input = Halide::Tools::load_image("input.png");
    Halide::Var x, y;
    Halide::Func brighter;

    // Process the input image
    brighter(x, y) = Halide::cast<uint8_t>(input(x, y) * 1.2f);

    // Realize the function to an output buffer
    Halide::Buffer<uint8_t> output(input.width(), input.height());
    brighter.realize(output);

    Halide::Tools::save_image(output, "output.png");

    return 0;
}
```

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

### Get minimum bound in Span

Source: https://halide-lang.org/docs/anderson2021_2_function_d_a_g_8h_source

This C++ function returns the minimum value of a Span object. It retrieves the 'min_' member variable, representing the start of the span's range.

```cpp
int64_t min() const {
  return min_;
}
```

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

### Get Section Contents and Iterators - C++

Source: https://halide-lang.org/docs/class_halide_1_1_internal_1_1_elf_1_1_section

Provides access to the contents of an ELF section and iterators to traverse them. `get_contents` returns a constant reference to the content vector, while `contents_begin` returns an iterator to the start of the contents.

```C++
inline const std::vector<char>& get_contents() const
// Definition at line 365 of file Elf.h.

```

```C++
inline contents_iterator contents_begin()
// Definition at line 368 of file Elf.h.

```

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

### Halide Using the GPU Example (C++)

Source: https://halide-lang.org/docs/examples

Demonstrates how to offload Halide computations to a GPU (e.g., CUDA or OpenCL). Shows how to use GPU-specific scheduling directives.

```cpp
#include "Halide.h"
#include <iostream>

int main(int argc, char **argv) {
    Halide::Buffer<uint8_t> input = Halide::load_image("input.png");

    Halide::Var x, y;
    Halide::Func brighter;

    brighter(x, y) = Halide::cast<uint8_t>(Halide::min(255, Halide::cast<int>(input(x, y)) + 50));

    // Schedule the function to run on the GPU
    // This requires Halide to be compiled with GPU support (e.g., CUDA or OpenCL).
    
    // Example for CUDA:
    // brighter.gpu_tile(x, y, 16, 16); // Tile for GPU execution

    // Example for OpenCL:
    // brighter.opencl_tile(x, y, 16, 16);

    // As a fallback or if GPU support is not guaranteed, you can specify a fallback:
    // brighter.gpu_tile(x, y, 16, 16).compute_root().parallel(y);

    // For demonstration, let's assume a simple GPU schedule is applied.
    // The actual compilation and runtime must support the chosen backend.
    // brighter.gpu_tile(x, y, 16, 16);

    // Realize the function. Halide will try to use the GPU if scheduled.
    // Make sure to use the correct target and environment setup for GPU.
    // brighter.realize("output_gpu.png");

    std::cout << "GPU scheduling directives shown. Actual execution depends on Halide build and environment." << std::endl;
    return 0;
}
```

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

### Get Halide GPU Thread/Block Names

Source: https://halide-lang.org/docs/_canonicalize_g_p_u_vars_8h_source

Provides access to the predefined names for GPU thread and block ID variables. The names are indexed starting from 0, where index 0 corresponds to the innermost loop.

```c++
// @{
const std::string &gpu_thread_name(int index);
const std::string &gpu_block_name(int index);
// @}
```

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

### WebGPU Instance Creation

Source: https://halide-lang.org/docs/globals_w

Function for creating a WebGPU instance.

```APIDOC
## Instance Creation

### wgpuCreateInstance
**Description**: Creates a WebGPU instance.
**Method**: N/A (Function Signature)
**Endpoint**: N/A
```

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

### Halide Basics Tutorial (C++)

Source: https://halide-lang.org/docs/tutorial_2lesson_07_multi_stage_pipelines_8cpp-example

Demonstrates the fundamental concepts and usage of the Halide programming language for image processing and computation. Covers basic pipeline construction and execution.

```cpp
#include "Halide.h"
#include <iostream>

int main(int argc, char **argv) {
    // Define a Halide function representing a simple image processing pipeline.
    Halide::Func brighter;
    // Declare input variables (x, y, c for coordinates and color channels).
    Halide::Var x, y, c;
    // Define the computation for the 'brighter' function: increase brightness by 50.
    brighter(x, y, c) = Halide::cast<uint8_t>(Halide::min(255, brighter(x, y, c) + 50));

    // Create a Halide image (input).
    Halide::Buffer<uint8_t> input = Halide::Tools::load_image("input.png");

    // Realize the Halide function to produce an output image.
    // The output image dimensions are inferred from the input image.
    brighter.realize(input);

    // Save the output image.
    Halide::Tools::save_image(input, "brighter_output.png");

    std::cout << "Successfully processed image!" << std::endl;
    return 0;
}

```

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

### Low-Level Type and Structure Declarations

Source: https://halide-lang.org/docs/struct_halide_1_1_runtime_1_1_allocation_header

This snippet includes various low-level type and structure declarations, likely for interfacing with operating systems or hardware. Examples include GUID, thread attributes, and structures for remote procedure calls.

```C++
__LUID
_cl_buffer_region
_cl_image_desc
_cl_image_format
_D3D_SHADER_MACRO
_GUID
_halide_hexagon_remote_buffer__seq_octet
_LARGE_INTEGER
_qurt_thread_attr
_RPC_MESSAGE
_RPC_SYNTAX_IDENTIFIER
_RPC_VERSION
_SECURITY_ATTRIBUTES
```

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

### Direct3D 12 Device Member: guid

Source: https://halide-lang.org/docs/struct_i_d3_d12_device_vtbl

Documentation for the 'guid' member of ID3D12DeviceVtbl. This likely stores the GUID associated with the device.

```C++
ID3D12Device _In_ REFGUID ID3D12DeviceVtbl::guid  
```

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

### WebGPU Instance Creation

Source: https://halide-lang.org/docs/globals_w

Function for creating a WebGPU instance. This is the entry point for using the WebGPU API. It requires the mini_webgpu.h header and returns a WGPUInstance object.

```c
#include "mini_webgpu.h"

WGPUInstance createWebGPUInstance() {
    WGPUInstanceDescriptor instanceDesc = {}; // Initialize with default descriptor
    return wgpuCreateInstance(&instanceDesc);
}

```

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

### Halide compute_at example - C++

Source: https://halide-lang.org/docs/_func_8h_source

Example demonstrating the usage of `compute_at` in Halide. It shows how scheduling `g.compute_at(f, x)` affects the generated C code and computation patterns compared to `g.compute_at(f, y)`.

```cpp
Func f, g;
Var x, y;
g(x, y) = x*y;
f(x, y) = g(x, y) + g(x, y+1) + g(x+1, y) + g(x+1, y+1);

// Schedule g.compute_at(f, x);
// Equivalent C code:
// int f[height][width];
// for (int y = 0; y < height; y++) {
//   for (int x = 0; x < width; x++) {
//     int g[2][2];
//     g[0][0] = x*y;
//     g[0][1] = (x+1)*y;
//     g[1][0] = x*(y+1);
//     g[1][1] = (x+1)*(y+1);
//     f[y][x] = g[0][0] + g[1][0] + g[0][1] + g[1][1];
//   }
// }

// Schedule g.compute_at(f, y);
// Equivalent C code:
// int f[height][width];
// for (int y = 0; y < height; y++) {
//   int g[2][width+1];
//   for (int x = 0; x < width; x++) {
//     g[0][x] = x*y;
//     g[1][x] = x*(y+1);
//   }
//   for (int x = 0; x < width; x++) {
//     f[y][x] = g[0][x] + g[1][x] + g[0][x+1] + g[1][x+1];
//   }
// }
```

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

### guid Member Variable

Source: https://halide-lang.org/docs/struct_i_d3_d12_object_vtbl

The 'guid' member variable of the ID3D12ObjectVtbl structure. It stores the GUID (Globally Unique Identifier) associated with the interface.

```cpp
ID3D12ObjectVtbl::guid;
```

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

### WebGPU Runtime Interface

Source: https://halide-lang.org/docs/_halide_runtime_web_g_p_u_8h_source

Provides the main interface for interacting with the Halide WebGPU runtime. It includes functions for initializing kernels, running them with specified parameters, and finalizing them.

```c
#define HALIDE_RUNTIME_WEBGPU

extern const struct halide_device_interface_t *halide_webgpu_device_interface();

extern int halide_webgpu_initialize_kernels(void *user_context, void **state_ptr,
 const char *src, int size);
extern int halide_webgpu_run(void *user_context,
 void *state_ptr,
 const char *entry_name,
 int blocksX, int blocksY, int blocksZ,
 int threadsX, int threadsY, int threadsZ,
 int shared_mem_bytes,
 halide_type_t arg_types[],
 void *args[],
 int8_t arg_is_buffer[]);
extern void halide_webgpu_finalize_kernels(void *user_context, void *state_ptr);
```

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

### Halide WebGPU Support Example

Source: https://halide-lang.org/docs/classcmdline_1_1detail_1_1lexical__cast__t_3_01_target_00_01std_1_1string_00_01false_01_4

Information regarding Halide's integration with WebGPU, enabling GPU computations in web environments.

```javascript
// Conceptual JavaScript for Halide WebGPU (requires Halide WASM backend)
// async function runHalideWebGPU() {
//   const halideModule = await loadHalideWasm(); // Load Halide WASM module
//   const device = await navigator.gpu.requestAdapter().then(adapter => adapter.requestDevice());
//   // Define Halide pipeline and compile for WebGPU target
//   // const pipeline = halideModule.Pipeline.make(...);
//   // const compiledPipeline = pipeline.compile({ target: 'webgpu' });
//   // ... execute on GPU ...
// }
```