package neural_nets_lib

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val unconstrained : row_constraint Variantslib.Variant.t
val total_elems : (nominator:Base.int -> divided_by:dim_var_set -> row_constraint) Variantslib.Variant.t
val fold : init:'acc__0 -> unconstrained:('acc__0 -> row_constraint Variantslib.Variant.t -> 'acc__1) -> total_elems: ('acc__1 -> (nominator:Base.int -> divided_by:dim_var_set -> row_constraint) Variantslib.Variant.t -> 'acc__2) -> 'acc__2
val iter : unconstrained:(row_constraint Variantslib.Variant.t -> Base.unit) -> total_elems: ((nominator:Base.int -> divided_by:dim_var_set -> row_constraint) Variantslib.Variant.t -> Base.unit) -> Base.unit
val map : row_constraint -> unconstrained:(row_constraint Variantslib.Variant.t -> 'result__) -> total_elems: ((nominator:Base.int -> divided_by:dim_var_set -> row_constraint) Variantslib.Variant.t -> nominator:Base.int -> divided_by:dim_var_set -> 'result__) -> 'result__
val make_matcher : unconstrained: (row_constraint Variantslib.Variant.t -> 'acc__0 -> (Base.unit -> 'result__) * 'acc__1) -> total_elems: ((nominator:Base.int -> divided_by:dim_var_set -> row_constraint) Variantslib.Variant.t -> 'acc__1 -> (nominator:Base.int -> divided_by:dim_var_set -> 'result__) * 'acc__2) -> 'acc__0 -> (row_constraint -> 'result__) * 'acc__2
val to_rank : row_constraint -> Base.int
val to_name : row_constraint -> Base.string
val descriptions : (Base.string * Base.int) Base.list
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