package neural_nets_lib

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val term : label:Base.string Base.list -> ?batch_dims:Base.int Base.list -> ?input_dims:Base.int Base.list -> ?output_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 -> ?init_op:Tensor.init_op -> ?fetch_op:(v:Tensor.tn -> Tensor.fetch_op) -> Base.unit -> Tensor.t
val number : ?label:Base.string Base.list -> ?axis_label:Base.string -> Base.float -> Tensor.t
val ndarray : ?label:Base.string Base.list -> ?batch_dims:Base.int Base.list -> ?input_dims:Base.int Base.list -> ?output_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 -> ?strict:Base.bool -> Base.float Base.array -> Tensor.t
val param : ?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 -> ?strict:Base.bool -> ?values:Base.float Base.array -> Base.string -> Tensor.t
module O : sig ... end
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