object Model

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  1. case class EvalOps[In, Out](inputIterator: DatasetIterator[In], input: In, output: Out, metricValues: Seq[ops.Output[Float]], metricUpdates: Seq[ops.Output[Float]], metricResets: Set[ops.UntypedOp]) extends Product with Serializable
  2. case class InferOps[In, Out](inputIterator: DatasetIterator[In], input: In, output: Out) extends Product with Serializable
  3. case class TrainOps[TrainIn, TrainOut, Loss](inputIterator: DatasetIterator[TrainIn], input: TrainIn, output: TrainOut, loss: ops.Output[Loss], gradientsAndVariables: Seq[(ops.OutputLike[Loss], ops.variables.Variable[Any])], trainOp: ops.UntypedOp)(implicit evidence$7: core.types.TF[Loss], evidence$8: core.types.IsFloatOrDouble[Loss]) extends Product with Serializable

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  1. final def !=(arg0: Any): Boolean
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  14. final def notifyAll(): Unit
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  15. def simpleSupervised[In, TrainIn, Out, TrainOut, Loss](input: Input[In], trainInput: Input[TrainIn], layer: Layer[In, Out], loss: Layer[(Out, TrainIn), ops.Output[Loss]], optimizer: Optimizer, clipGradients: ClipGradients = NoClipGradients, colocateGradientsWithOps: Boolean = false)(implicit arg0: core.types.TF[Loss], arg1: core.types.IsFloatOrDouble[Loss], evOutputToDataTypeIn: Aux[In, _], evOutputToShapeIn: Aux[In, _], evOutputToDataTypeTrainIn: Aux[TrainIn, _], evOutputToShapeTrainIn: Aux[TrainIn, _]): SupervisedTrainableModel[In, TrainIn, Out, Out, Loss]
  16. def supervised[In, TrainIn, Out, TrainOut, Loss](input: Input[In], trainInput: Input[TrainIn], layer: Layer[In, Out], trainLayer: Layer[(In, TrainIn), TrainOut], loss: Layer[(TrainOut, (In, TrainIn)), ops.Output[Loss]], optimizer: Optimizer, clipGradients: ClipGradients = NoClipGradients, colocateGradientsWithOps: Boolean = false)(implicit arg0: core.types.TF[Loss], arg1: core.types.IsFloatOrDouble[Loss], evOutputToDataTypeIn: Aux[In, _], evOutputToShapeIn: Aux[In, _], evOutputToDataTypeTrainIn: Aux[TrainIn, _], evOutputToShapeTrainIn: Aux[TrainIn, _]): SupervisedTrainableModel[In, TrainIn, Out, TrainOut, Loss]
  17. final def synchronized[T0](arg0: ⇒ T0): T0
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  18. def toString(): String
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  19. def unsupervised[In, Out, Loss](input: Input[In], layer: Layer[In, Out], loss: Layer[(In, Out), ops.Output[Loss]], optimizer: Optimizer, clipGradients: ClipGradients = NoClipGradients, colocateGradientsWithOps: Boolean = false)(implicit arg0: core.types.TF[Loss], arg1: core.types.IsFloatOrDouble[Loss], evOutputToDataTypeIn: Aux[In, _], evOutputToShapeIn: Aux[In, _]): UnsupervisedTrainableModel[In, Out, Loss]
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  21. final def wait(arg0: Long, arg1: Int): Unit
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  22. final def wait(arg0: Long): Unit
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