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      "summary": "",
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              "name": "encoder",
              "params": [],
              "raises": [],
              "returns": [],
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                "path": "helia_edge/trainers/gs_autoencoder.py",
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              "name": "gs",
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              "name": "decoder",
              "params": [],
              "raises": [],
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              "name": "metrics",
              "params": [],
              "raises": [],
              "returns": [],
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                "path": "helia_edge/trainers/gs_autoencoder.py",
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              "kind": "method",
              "language": "python",
              "members": [],
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                  "description": "Input batch.",
                  "name": "x",
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                  "type": "bool"
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                  "type": "bool"
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                  "default": "False",
                  "description": "If True, also return code probabilities.",
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                  "type": "bool"
                }
              ],
              "raises": [],
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                  "description": "Reconstruction, optionally with indices and/or probabilities.",
                  "type": "keras.KerasTensor | tuple[keras.KerasTensor, ...]"
                }
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                  "type": "keras.losses.Loss | None"
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                  "default": "None",
                  "description": "Standard Keras metrics (Metric instances or callables).",
                  "name": "metrics",
                  "type": "list | None"
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                  "description": "List of callables (y_true, y_pred) -> scalar to add to loss.",
                  "name": "extra_losses",
                  "type": "list | None"
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                  "description": "Metric instances or callables (y_true, y_pred) -> scalar.",
                  "name": "extra_metrics",
                  "type": "list | None"
                }
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              "returns": [],
              "signature": "compile(\n    optimizer: keras.optimizers.Optimizer,\n    loss: keras.losses.Loss | None = None,\n    metrics: list | None = None,\n    extra_losses: list | None = None,\n    extra_metrics: list | None = None,\n    **kwargs={},\n)",
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                "url": "https://github.com/AmbiqAI/helia-edge/blob/f341fb11f7f5d77a4974ba8273c4cd55d67c21f0/helia_edge/trainers/gs_autoencoder.py#L73"
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            },
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              "id": "helia_edge.trainers.gs_autoencoder.GSAutoencoder.compute_loss",
              "kind": "method",
              "language": "python",
              "members": [],
              "name": "compute_loss",
              "params": [],
              "raises": [],
              "returns": [],
              "signature": "compute_loss(x=None, y=None, y_pred=None, sample_weight=None, allow_empty=False)",
              "source": {
                "line": 106,
                "path": "helia_edge/trainers/gs_autoencoder.py",
                "url": "https://github.com/AmbiqAI/helia-edge/blob/f341fb11f7f5d77a4974ba8273c4cd55d67c21f0/helia_edge/trainers/gs_autoencoder.py#L106"
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            },
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              "description": "Update compiled metrics plus extra metric trackers.",
              "examples": [],
              "id": "helia_edge.trainers.gs_autoencoder.GSAutoencoder.compute_metrics",
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              "members": [],
              "name": "compute_metrics",
              "params": [],
              "raises": [],
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            },
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              "examples": [],
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              "language": "python",
              "members": [],
              "name": "get_config",
              "params": [],
              "raises": [],
              "returns": [],
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              "source": {
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                "url": "https://github.com/AmbiqAI/helia-edge/blob/f341fb11f7f5d77a4974ba8273c4cd55d67c21f0/helia_edge/trainers/gs_autoencoder.py#L140"
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            },
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              "description": "`classmethod`\n\nRecreate model from serialized config.",
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              "kind": "method",
              "language": "python",
              "members": [],
              "name": "from_config",
              "params": [],
              "raises": [],
              "returns": [],
              "signature": "from_config(config, custom_objects=None)",
              "source": {
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                "path": "helia_edge/trainers/gs_autoencoder.py",
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              "description": "Encoder model producing continuous latents.",
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            {
              "description": "Decoder model mapping bottleneck outputs to reconstructions.",
              "name": "decoder",
              "type": "keras.Model"
            }
          ],
          "raises": [],
          "returns": [],
          "signature": "GSAutoencoder(encoder: keras.Model, gs: GumbelSoftmaxBottleneck, decoder: keras.Model, **kwargs={})",
          "source": {
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          },
          "summary": "Convenience wrapper around (encoder -> GumbelSoftmaxBottleneck -> decoder)."
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}