Variants_of_bcast (neural_nets_lib.Ocannl.Row.Variants_of

val row_var : 
  (v:row_var -> beg_dims:dim Base.list -> bcast) Variantslib.Variant.t

val broadcastable : bcast Variantslib.Variant.t

val fold : 
  init:'acc__0 ->
  row_var:
    ('acc__0 ->
      (v:row_var -> beg_dims:dim Base.list -> bcast) Variantslib.Variant.t ->
      'acc__1) ->
  broadcastable:('acc__1 -> bcast Variantslib.Variant.t -> 'acc__2) ->
  'acc__2

val iter : 
  row_var:
    ((v:row_var -> beg_dims:dim Base.list -> bcast) Variantslib.Variant.t ->
      Base.unit) ->
  broadcastable:(bcast Variantslib.Variant.t -> Base.unit) ->
  Base.unit

val map : 
  bcast ->
  row_var:
    ((v:row_var -> beg_dims:dim Base.list -> bcast) Variantslib.Variant.t ->
      v:row_var ->
      beg_dims:dim Base.list ->
      'result__) ->
  broadcastable:(bcast Variantslib.Variant.t -> 'result__) ->
  'result__

val make_matcher : 
  row_var:
    ((v:row_var -> beg_dims:dim Base.list -> bcast) Variantslib.Variant.t ->
      'acc__0 ->
      (v:row_var -> beg_dims:dim Base.list -> 'result__) * 'acc__1) ->
  broadcastable:
    (bcast Variantslib.Variant.t ->
      'acc__1 ->
      (Base.unit -> 'result__) * 'acc__2) ->
  'acc__0 ->
  (bcast -> 'result__) * 'acc__2

val to_rank : bcast -> Base.int

val to_name : bcast -> Base.string

val descriptions : (Base.string * Base.int) Base.list