_allow_dotted_keys:
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_convert_dict:
    value: true
_fields:
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        evals: |
            val:
              data:
                name: imagenet2012
                split: validation
              log_steps: 2500
              loss_name: softmax_xent
              pp_fn: decode|resize_small(256)|central_crop(224)|value_range(-1, 1)|onehot(1000,
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              type: classification
        grad_clip_norm: 1
        input: |
            accum_freq: 8
            batch_size: 1024
            cache_raw: false
            data:
              name: imagenet2012
              split: train
            pp: decode_jpeg_and_inception_crop(224)|flip_lr|randaug(2,10)|value_range(-1, 1)|onehot(1000,
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            shuffle_buffer_size: 150000
        log_training_steps: 50
        loss: softmax_xent
        lr: 0.001
        mixup: |
            fold_in: null
            p: 0.2
        model: |
            pool_type: gap
            posemb: sincos2d
            rep_size: false
            variant: S/16
        model_name: vit
        num_classes: 1000
        optax: |
            mu_dtype: bfloat16
        optax_name: scale_by_adam
        pp_modules:
            - ops_general
            - ops_image
            - ops_text
            - archive.randaug
        schedule: |
            decay_type: cosine
            warmup_steps: 10000
        seed: 0
        total_epochs: 90
        wd: 0.0001
_locked:
    value: true
_sort_keys:
    value: true
_type_safe:
    value: true
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        python_version: 3.11.10
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