Variants_of_row_constraint (neural_nets_lib.Ocannl.Row.Variants_of_row

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