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# CV_GUIDES_MASTER = {
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#     "path": "keras_cv/",
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#     "title": "KerasCV",
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#     "toc": True,
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#     "children": [
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#         {
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#             "path": "object_detection_keras_cv",
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#             "title": "Use KerasCV to assemble object detection pipelines",
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#         },
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#         {
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#             "path": "classification_with_keras_cv",
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#             "title": "Use KerasCV to train powerful image classifiers.",
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#         },
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#         {
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#             "path": "cut_mix_mix_up_and_rand_augment",
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#             "title": "CutMix, MixUp, and RandAugment image augmentation with KerasCV",
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#         },
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#         {
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#             "path": "generate_images_with_stable_diffusion",
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#             "title": "High-performance image generation using Stable Diffusion in KerasCV",
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#         },
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#         {
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#             "path": "custom_image_augmentations",
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#             "title": "Custom Image Augmentations with BaseImageAugmentationLayer",
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#         },
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#         {
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#             "path": "semantic_segmentation_deeplab_v3_plus",
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#             "title": "Semantic Segmentation with KerasCV",
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#         },
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#         {
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#             "path": "segment_anything_in_keras_cv",
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#             "title": "Segment Anything in KerasCV",
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#         },
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#     ],
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# }
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GUIDES_MASTER = {
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    "path": "guides/",
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    "title": "Developer guides",
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    "toc": True,
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    "children": [
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        {
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            "path": "functional_api",
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            "title": "The Functional API",
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        },
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        {
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            "path": "sequential_model",
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            "title": "The Sequential model",
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        },
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        {
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            "path": "making_new_layers_and_models_via_subclassing",
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            "title": "Making new layers & models via subclassing",
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        },
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        {
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            "path": "training_with_built_in_methods",
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            "title": "Training & evaluation with the built-in methods",
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        },
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        {
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            "path": "custom_train_step_in_jax",
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            "title": "Customizing `fit()` with JAX",
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        },
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        {
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            "path": "custom_train_step_in_tensorflow",
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            "title": "Customizing `fit()` with TensorFlow",
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        },
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        {
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            "path": "custom_train_step_in_torch",
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            "title": "Customizing `fit()` with PyTorch",
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        },
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        {
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            "path": "writing_a_custom_training_loop_in_jax",
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            "title": "Writing a custom training loop in JAX",
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        },
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        {
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            "path": "writing_a_custom_training_loop_in_tensorflow",
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            "title": "Writing a custom training loop in TensorFlow",
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        },
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        {
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            "path": "writing_a_custom_training_loop_in_torch",
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            "title": "Writing a custom training loop in PyTorch",
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        },
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        {
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            "path": "serialization_and_saving",
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            "title": "Serialization & saving",
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        },
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        {
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            "path": "customizing_saving_and_serialization",
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            "title": "Customizing saving & serialization",
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        },
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        {
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            "path": "writing_your_own_callbacks",
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            "title": "Writing your own callbacks",
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        },
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        {
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            "path": "transfer_learning",
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            "title": "Transfer learning & fine-tuning",
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        },
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        {
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            "path": "distributed_training_with_jax",
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            "title": "Distributed training with JAX",
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        },
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        {
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            "path": "distributed_training_with_tensorflow",
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            "title": "Distributed training with TensorFlow",
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        },
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        {
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            "path": "distributed_training_with_torch",
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            "title": "Distributed training with PyTorch",
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        },
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        {
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            "path": "distribution",
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            "title": "Distributed training with Keras 3",
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        },
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        {
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            "path": "migrating_to_keras_3",
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            "title": "Migrating Keras 2 code to Keras 3",
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        },
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        {
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            "path": "keras_nnx_guide",
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            "title": "How to use Keras with NNX backend",
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        },
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        {
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            "path": "orbax_checkpoint",
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            "title": "Orbax Checkpointing in Keras",
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        },
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        {
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            "path": "quantization_overview",
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            "title": "Quantization in Keras",
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        },
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        {
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            "path": "int8_quantization_in_keras",
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            "title": "8-bit integer quantization in Keras",
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        },
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        {
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            "path": "int4_quantization_in_keras",
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            "title": "4-bit integer quantization in Keras",
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        }
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        ,
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        {
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            "path": "gptq_quantization_in_keras",
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            "title": "GPTQ quantization in Keras",
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        },
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        {
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            "path": "writing_quantization_compatible_layers",
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            "title": "Writing quantization-compatible layers in Keras",
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        }
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        # {
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        #     "path": "preprocessing_layers",
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        #     "title": "Working with preprocessing layers",
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        # },
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        # {
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        #     "path": "working_with_rnns",
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        #     "title": "Working with recurrent neural networks",
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        # },
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        # {
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        #     "path": "understanding_masking_and_padding",
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        #     "title": "Understanding masking & padding",
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        # },
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        # {
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        #     'path': 'writing_your_own_metrics',
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        #     'title': 'Writing your own Metrics',
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        # },
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        # {
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        #     'path': 'writing_your_own_losses',
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        #     'title': 'Writing your own Losses',
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        # },
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        # {
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        #     'path': 'tpu_training',
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        #     'title': 'Training Keras models on TPU',
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        # },
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        # {
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        #     'path': 'hyperparameter_optimization',
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        #     'title': 'Hyperparameter optimization',
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        # },
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        # {
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        #     'path': 'mixed_precision',
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        #     'title': 'Mixed precision training',
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        # },
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        # CV_GUIDES_MASTER,
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    ],
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}
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