Provides 'R' bindings to the 'GGML' tensor library for machine
    learning, designed primarily for 'Vulkan' GPU acceleration with full CPU
    fallback. Requires 'Vulkan' 1.2+ with legacy pipeline barriers (avoids
    'Synchronization2' due to 'RADV' performance issues); supports 'Push
    Descriptors' ('VK_KHR_push_descriptor')
    to eliminate descriptor pool overhead when available. 'Vulkan' support is
    auto-detected at build time on Linux (when 'libvulkan-dev' and 'glslc'
    are installed) and on Windows (when 'Vulkan' 'SDK' is installed and
    'VULKAN_SDK' environment variable is set); all operations fall back to
    CPU transparently when no GPU is available.
    Supports tensors up to 5D natively (GGML_MAX_DIMS=5).
    Implements tensor operations, neural network layers, 'quantization', and a
    'Keras'-like sequential model API for building and training networks.
    Includes 'AdamW' (Adam with Weight decay) and 'SGD' (Stochastic Gradient
    Descent) optimizers with 'MSE' (Mean Squared Error) and cross-entropy
    losses. Also provides a dynamic 'autograd' engine ('PyTorch'-style) with
    data-parallel training via 'dp_train()', broadcast arithmetic, 'f16'
    (half-precision) support on 'Vulkan' GPU, and a multi-head attention layer
    for building Transformer architectures. Supports 'ONNX' model import via
    built-in zero-dependency 'protobuf' parser: load 'pretrained' 'ONNX' models
    from 'PyTorch', 'TensorFlow', or other frameworks and run inference on
    'Vulkan' GPU or CPU. Covers 50+ 'ONNX' ops including convolutions,
    attention primitives, normalization, quantized ops, shape operations,
    'ScatterElements' (with 'Vulkan' 'atomicAdd' for GNN scatter-add),
    and fused custom ops (RelPosBias2D for 'BoTNet') — sufficient to run
    real-world models such as 'RoBERTa', 'BERT', 'GPT-NeoX', 'SqueezeNet',
    'Inception v3', 'BAT-ResNeXt', 'BoTNet', and 'MNIST' out of the box.
    Reads 'GGUF' files natively: load 'pretrained' weights from any
    'gguf'-compatible source ('llama.cpp', 'Hugging Face') with automatic
    weight conversion and metadata access.
    Uses a dedicated weight buffer architecture for zero-overhead repeated
    inference — weights are loaded to GPU once and never re-transferred.
    Serves as backend for 'LLM' (Large Language Model)
    inference via 'llamaR' and Stable Diffusion image generation via 'sd2R'.
    See <https://github.com/ggml-org/ggml> for more information about the
    underlying library.