              "\n\tmax_pooling: Use a Global Max Pooling 2D layer for the bottleneck."),
          "datatype": str,
          "group": "bottleneck",
          "gui_radio": True,
          "choices": ["average_pooling", "dense", "max_pooling"],
          "fixed": True},
      "bottleneck_norm": {
          "default": "none",
          "info": (
              "Apply a normalization layer after encoder output and prior to the bottleneck."
 
</s> remove             "\n\tmax_pooling: Use a Global Max Pooling 2D layer for the bottleneck."),
 </s> add             "\n\tmax_pooling: Use a Global Max Pooling 2D layer for the bottleneck."
            "\n\flatten: Don't use a bottleneck at all. Some encoders output in a size that make "
            "a bottleneck unnecessary. This option flattens the output from the encoder, with no "
            "further operations"), </s> remove         "min_max": (0, 100),
 </s> add         "min_max": (0, 200), </s> remove     bottleneck: str
        The type of layer to use for the bottleneck
 </s> add     bottleneck: str or ``None``
        The type of layer to use for the bottleneck. ``None`` to not use a bottleneck </s> remove                 var_x = LeakyReLU(alpha=0.1)(var_x)
 </s> add                 var_x = kl.LeakyReLU(alpha=0.1)(var_x)
        "choices": ["average_pooling", "dense", "max_pooling", "flatten"],
              "\n\tmax_pooling: Use a Global Max Pooling 2D layer for the bottleneck."),
          "datatype": str,
          "group": "bottleneck",
          "gui_radio": True,
        "choices": ["average_pooling", "dense", "max_pooling", "flatten"],
          "fixed": True},
      "bottleneck_norm": {
          "default": "none",
          "info": (
              "Apply a normalization layer after encoder output and prior to the bottleneck."
keep keep keep keep replace keep keep keep keep keep
 Updates:
    - Remove clipfaker
    - Add clip to Phaze-A
    - streamline clip to vision only
    - update lib.model.networks
    - Docs and tests update
    - Phaze-A - add option to flatten bottleneck 
plugins/train/model/phaze_a_defaults.py