              "architecture tweaking.\nNB: For keras based pre-built models, the global "
              "initializers and padding options will be ignored for the selected encoder."
              "\n\n\tdensenet: (32px -224px). Ref: Densely Connected Convolutional Networks "
              "(2016): https://arxiv.org/abs/1608.06993?source=post_page"
              "\n\n\tefficientnet: [Tensorflow 2.3+ only] EfficientNet has numerous variants (B0 - "
              "B8) that increases the model width, depth and dimensional space at each step. The "
              "minimum input resolution is 32px for all variants. The maximum input resolution for "
              "each variant is: b0: 224px, b1: 240px, b2: 260px, b3: 300px, b4: 380px, b5: 456px, "
 
</s> remove         "min_max": (0, 100),
 </s> add         "min_max": (0, 200), </s> remove     def build_model(self, inputs: list[Tensor]) -> keras.models.Model:
 </s> add     def build_model(self, inputs: list[tf.Tensor]) -> tf.keras.models.Model: </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         inputs = Input(shape=self._input_shape)
 </s> add         inputs = kl.Input(shape=self._input_shape) </s> remove     def _reshape_for_output(self, inputs: Tensor) -> Tensor:
 </s> add     def _reshape_for_output(self, inputs: tf.Tensor) -> tf.Tensor:
            "\n\n\tCLIPv: This is an implementation of the Visual encoder from the CLIP "
            "transformer. The ViT weights are trained on imagenet whilst the FaRL weights are "
            "trained on face related tasks. All have a default input size of 224px except for "
            "ViT-L-14-336px that has an input size of 336px. Ref: Learning Transferable Visual "
            "Models From Natural Language Supervision (2021): https://arxiv.org/abs/2103.00020"
              "architecture tweaking.\nNB: For keras based pre-built models, the global "
              "initializers and padding options will be ignored for the selected encoder."
            "\n\n\tCLIPv: This is an implementation of the Visual encoder from the CLIP "
            "transformer. The ViT weights are trained on imagenet whilst the FaRL weights are "
            "trained on face related tasks. All have a default input size of 224px except for "
            "ViT-L-14-336px that has an input size of 336px. Ref: Learning Transferable Visual "
            "Models From Natural Language Supervision (2021): https://arxiv.org/abs/2103.00020"
              "\n\n\tdensenet: (32px -224px). Ref: Densely Connected Convolutional Networks "
              "(2016): https://arxiv.org/abs/1608.06993?source=post_page"
              "\n\n\tefficientnet: [Tensorflow 2.3+ only] EfficientNet has numerous variants (B0 - "
              "B8) that increases the model width, depth and dimensional space at each step. The "
              "minimum input resolution is 32px for all variants. The maximum input resolution for "
              "each variant is: b0: 224px, b1: 240px, b2: 260px, b3: 300px, b4: 380px, b5: 456px, "
keep add keep 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