PDSL (neural_nets_lib.Ocannl_tensor.Operation.DSL

val grad_spec : Tensor.grad_spec

val term : 
  ?init_data:Ir.Assignments.init_data ->
  ?fetch_op:Tensor.fetch_op ->
  Tensor.op_fun

val number : 
  ?label:Base.string Base.list ->
  ?axis_label:Base.string ->
  Base.float ->
  Tensor.t

val bits : 
  ?label:Base.string Base.list ->
  ?axis_label:Base.string ->
  Base.int64 ->
  Tensor.t

val ndarray : Base.float Base.array -> Tensor.op_fun

val threefry4x32 : 
  Tensor.t ->
  Tensor.t ->
  ?label:Base.string Base.list ->
  ?batch_dims:Base.int Base.list ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  ?input_dims:Base.int Base.list ->
  ?output_dims:Base.int Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?deduced:Shape.deduce_within_shape ->
  unit ->
  Tensor.t

val uint4x32_to_prec_uniform : 
  Tensor.t ->
  ?label:Base.string Base.list ->
  ?top_down_prec:Base.bool ->
  ?batch_dims:Base.int Base.list ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  Tensor.param_op_fun

val uint4x32_to_prec_uniform1 : 
  Tensor.t ->
  ?label:Base.string Base.list ->
  ?top_down_prec:Base.bool ->
  ?batch_dims:Base.int Base.list ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  Tensor.param_op_fun

val embed_self_id : ?label:Base.string list -> unit -> Tensor.t

val default_param_init : 
  (unit ->
    ?label:Base.string Base.list ->
    ?top_down_prec:Base.bool ->
    ?batch_dims:Base.int Base.list ->
    ?batch_axes:(Base.string * Base.int) Base.list ->
    Tensor.param_op_fun)
    Base.ref

val param : 
  ?value:Base.float ->
  ?values:Base.float Base.array ->
  ?param_init:Tensor.op_fun ->
  Base.string ->
  ?more_label:Base.string Base.list ->
  Tensor.param_op_fun

val einsum : 
  ?capture_dims:Shape.delayed_var_ref Base.list ->
  Base.string ->
  Tensor.t ->
  Tensor.t ->
  Tensor.op_fun

val outer_sum : 
  ?capture_dims:Shape.delayed_var_ref Base.list ->
  Base.string ->
  Tensor.t ->
  Tensor.t ->
  Tensor.op_fun

val einsum1 : 
  ?capture_dims:Shape.delayed_var_ref Base.list ->
  Base.string ->
  Tensor.t ->
  Tensor.op_fun

val einmax1 : 
  ?capture_dims:Shape.delayed_var_ref Base.list ->
  Base.string ->
  Tensor.t ->
  Tensor.op_fun

val tropical : 
  ?capture_dims:Shape.delayed_var_ref Base.list ->
  Base.string ->
  Tensor.t ->
  Tensor.t ->
  Tensor.op_fun

val offsets : Tensor.op_fun

val range : 
  ?label:Base.string list ->
  ?axis_label:Base.string ->
  Base__Int.t ->
  Tensor.t

val range_of_shape : 
  ?label:Base.string list ->
  ?batch_dims:Base.Int.t Base.List.t ->
  ?input_dims:Base.Int.t Base.List.t ->
  ?output_dims:Base.Int.t Base.List.t ->
  ?batch_axes:(Base.string * Base.Int.t) Base.List.t ->
  ?input_axes:(Base.string * Base.Int.t) Base.List.t ->
  ?output_axes:(Base.string * Base.Int.t) Base.List.t ->
  unit ->
  Tensor.t

val stop_gradient : Tensor.t -> Tensor.op_fun

val reshape : 
  l:Base.string ->
  ?b:Base.int Base.list ->
  ?i:Base.int Base.list ->
  ?o:Base.int Base.list ->
  Ir.Ndarray.t ->
  ?fetch_op:Tensor.fetch_op ->
  ?top_down_prec:Base.bool ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?deduced:Shape.deduce_within_shape ->
  Base.unit ->
  Tensor.t

val wrap : 
  l:Base.string ->
  ?b:Base.int Base.list ->
  ?i:Base.int Base.list ->
  ?o:Base.int Base.list ->
  Ir.Ndarray.t ->
  ?fetch_op:Tensor.fetch_op ->
  ?top_down_prec:Base.bool ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?deduced:Shape.deduce_within_shape ->
  Base.unit ->
  Tensor.t

val wrap_padded : 
  l:Base.string ->
  ?b:Base.int Base.list ->
  ?i:Base.int Base.list ->
  ?o:Base.int Base.list ->
  padding:Ir.Ops.axis_padding Base.array ->
  padded_value:Base.float ->
  Ir.Assignments.Nd.t ->
  ?fetch_op:Tensor.fetch_op ->
  ?top_down_prec:Base.bool ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?deduced:Shape.deduce_within_shape ->
  Base.unit ->
  Tensor.t

val rebatch : 
  l:Base.string ->
  Ir.Ndarray.t ->
  ?fetch_op:Tensor.fetch_op ->
  ?top_down_prec:Base.bool ->
  ?batch_dims:Base.int Base.list ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?deduced:Shape.deduce_within_shape ->
  Base.unit ->
  Tensor.t

val init : 
  l:Base.string ->
  prec:Ir.Ops.prec ->
  ?b:Base.int Base__List.t ->
  ?i:Base.int Base__List.t ->
  ?o:Base.int Base__List.t ->
  f:(Base.int Base.array -> Base.float) ->
  ?fetch_op:Tensor.fetch_op ->
  ?top_down_prec:Base.bool ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?deduced:Shape.deduce_within_shape ->
  Base.unit ->
  Tensor.t

val uniform : 
  unit ->
  ?label:Base.string Base.list ->
  ?top_down_prec:Base.bool ->
  ?batch_dims:Base.int Base.list ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  Tensor.param_op_fun

val reshape_param : 
  l:Base.string ->
  ?i:Base.int Base.list ->
  ?o:Base.int Base.list ->
  Ir.Ndarray.t ->
  ?more_label:Base.string Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?deduced:Shape.deduce_within_shape ->
  Base.unit ->
  Tensor.t

val wrap_param : 
  l:Base.string ->
  ?i:Base.int Base.list ->
  ?o:Base.int Base.list ->
  Ir.Ndarray.t ->
  ?more_label:Base.string Base.list ->
  ?input_axes:(Base.string * Base.int) Base.list ->
  ?output_axes:(Base.string * Base.int) Base.list ->
  ?deduced:Shape.deduce_within_shape ->
  Base.unit ->
  Tensor.t

val matmul : Tensor.t -> Tensor.t -> Tensor.op_fun

val pointmul : Tensor.t -> Tensor.t -> Tensor.op_fun

val add : Tensor.t -> Tensor.t -> Tensor.op_fun

val pointpow : Base.float -> Tensor.t -> Tensor.op_fun

val relu : Tensor.t -> Tensor.op_fun

val sat01 : Tensor.t -> Tensor.op_fun

val fma : Tensor.t -> Tensor.t -> Tensor.t -> Tensor.op_fun

val number_int : 
  ?label:Base.string Base.list ->
  ?axis_label:Base.string ->
  int ->
  Tensor.t

val embed_symbol : 
  ?label:Base.string list ->
  Ir.Indexing.static_symbol ->
  Tensor.t

val embed_dim : ?label:Base.string list -> Shape.delayed_var_ref -> Tensor.t

val sub : Tensor.t -> Tensor.t -> Tensor.op_fun

val pointdiv : Tensor.t -> Tensor.t -> Tensor.op_fun

val slice : Idx.static_symbol -> Tensor.t -> Tensor.op_fun

val exp : Tensor.t -> Tensor.op_fun

val log : Tensor.t -> Tensor.op_fun

val log2 : Tensor.t -> Tensor.op_fun

val sin : Tensor.t -> Tensor.op_fun

val cos : Tensor.t -> Tensor.op_fun

val neg : Tensor.t -> Tensor.op_fun

val sqrt : Tensor.t -> Tensor.op_fun

val recip : Tensor.t -> Tensor.op_fun

val recip_sqrt : Tensor.t -> Tensor.op_fun

val tanh : Tensor.t -> Tensor.op_fun

val where : Tensor.t -> Tensor.t -> Tensor.t -> Tensor.op_fun

val not : Tensor.t -> Tensor.op_fun

val lt : Tensor.t -> Tensor.t -> Tensor.op_fun

val eq : Tensor.t -> Tensor.t -> Tensor.op_fun

val ne : Tensor.t -> Tensor.t -> Tensor.op_fun

val uniform_at : 
  Tensor.t ->
  ?label:Base.string Base.list ->
  ?top_down_prec:Base.bool ->
  ?batch_dims:Base.int Base.list ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  Tensor.param_op_fun

val uniform1 : 
  unit ->
  ?label:Base.string Base.list ->
  ?top_down_prec:Base.bool ->
  ?batch_dims:Base.int Base.list ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  Tensor.param_op_fun

val uniform_at1 : 
  Tensor.t ->
  ?label:Base.string Base.list ->
  ?top_down_prec:Base.bool ->
  ?batch_dims:Base.int Base.list ->
  ?batch_axes:(Base.string * Base.int) Base.list ->
  Tensor.param_op_fun

module O : sig ... end