# `Latu.ML.Helper`
[🔗](https://github.com/zero-one-group/latu_ml/blob/v0.4.2/lib/latu/ml/helper.ex#L1)

An operator that is neither fitted nor applied: its work is one method on the server's own
helper object.

Two of them on Spark 4.2.0 — `PowerIterationClustering` and `PrefixSpan` — and they are
operators in every way but the one that matters to the wire. They carry a JVM class and a
param table like an estimator does, and PySpark models them as classes; but there is no
`Fit`, no `Transform` and nothing cached, because what they do is a `Fetch` on
`ConnectHelper` with their params as **positional arguments**.

    pic = Latu.ML.Clustering.power_iteration_clustering(k: 2, max_iter: 10)
    clusters = Latu.ML.assign_clusters(pic, edges)

That positional call is the whole difference from every other operator here, and it has one
consequence worth knowing: an `MlParams` map carries only what the caller set and the server
fills the rest, where a helper's arguments are all sent every time — so this package supplies
the defaults from the registry, and refuses rather than guessing where a param has none that
can be written down.

No uid: nothing on the wire carries one, because nothing on the wire is an operator.

# `t`

```elixir
@type t() :: %Latu.ML.Helper{
  class: String.t(),
  known: [Latu.ML.Param.t()],
  params: [Latu.ML.Plan.param()]
}
```

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*Consult [api-reference.md](api-reference.md) for complete listing*