package torch
Legend:
Library
Module
Module type
Parameter
Class
Class type
type t = unit Ctypes.ptr
val t : t Ctypes.typ
val new_tensor : (unit -> t F.return) F.result
val tensor_of_data : (unit Ctypes_static.ptr -> int64 Ctypes_static.ptr -> int -> int -> int -> t F.return) F.result
val copy_data : (t -> unit Ctypes_static.ptr -> int64 -> int -> unit F.return) F.result
val copy_ : (t -> t -> unit F.return) F.result
val float_vec : (float Ctypes_static.ptr -> int -> int -> t F.return) F.result
val int_vec : (int64 Ctypes_static.ptr -> int -> int -> t F.return) F.result
val device : (t -> int F.return) F.result
val defined : (t -> bool F.return) F.result
val num_dims : (t -> int F.return) F.result
val shape : (t -> int Ctypes_static.ptr -> unit F.return) F.result
val scalar_type : (t -> int F.return) F.result
val backward : (t -> int -> int -> unit F.return) F.result
val requires_grad : (t -> int F.return) F.result
val grad_set_enabled : (int -> int F.return) F.result
val get : (t -> int -> t F.return) F.result
val double_value : (t -> int Ctypes_static.ptr -> int -> float F.return) F.result
val int64_value : (t -> int Ctypes_static.ptr -> int -> int64 F.return) F.result
val double_value_set : (t -> int Ctypes_static.ptr -> int -> float -> unit F.return) F.result
val int64_value_set : (t -> int Ctypes_static.ptr -> int -> int64 -> unit F.return) F.result
val fill_double : (t -> float -> unit F.return) F.result
val fill_int64 : (t -> int64 -> unit F.return) F.result
val print : (t -> unit F.return) F.result
val to_string : (t -> int -> string F.return) F.result
val free : (t -> unit F.return) F.result
val run_backward : (t Ctypes_static.ptr -> int -> t Ctypes_static.ptr -> int -> t Ctypes_static.ptr -> int -> int -> unit F.return) F.result