package neural_nets_lib

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val batch : kind Variantslib.Variant.t
val input : kind Variantslib.Variant.t
val output : kind Variantslib.Variant.t
val fold : init:'acc__0 -> batch:('acc__0 -> kind Variantslib.Variant.t -> 'acc__1) -> input:('acc__1 -> kind Variantslib.Variant.t -> 'acc__2) -> output:('acc__2 -> kind Variantslib.Variant.t -> 'acc__3) -> 'acc__3
val iter : batch:(kind Variantslib.Variant.t -> Base.unit) -> input:(kind Variantslib.Variant.t -> Base.unit) -> output:(kind Variantslib.Variant.t -> Base.unit) -> Base.unit
val map : kind -> batch:(kind Variantslib.Variant.t -> 'result__) -> input:(kind Variantslib.Variant.t -> 'result__) -> output:(kind Variantslib.Variant.t -> 'result__) -> 'result__
val make_matcher : batch: (kind Variantslib.Variant.t -> 'acc__0 -> (Base.unit -> 'result__) * 'acc__1) -> input: (kind Variantslib.Variant.t -> 'acc__1 -> (Base.unit -> 'result__) * 'acc__2) -> output: (kind Variantslib.Variant.t -> 'acc__2 -> (Base.unit -> 'result__) * 'acc__3) -> 'acc__0 -> (kind -> 'result__) * 'acc__3
val to_rank : kind -> Base.int
val to_name : kind -> Base.string
val descriptions : (Base.string * Base.int) Base.list
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