}
	// match: (Zero {t1} [n] p1 store:(Store {t2} (OffPtr [o2] p2) _ mem))
	// cond: isSamePtr(p1, p2) && store.Uses == 1 && n >= o2 + t2.Size() && clobber(store)
	// result: (Zero {t1} [n] p1 mem)
	for {
		n := auxIntToInt64(v.AuxInt)
		t1 := auxToType(v.Aux)
		p1 := v_0
		store := v_1
		if store.Op != OpStore {
			break
		}
		t2 := auxToType(store.Aux)
		mem := store.Args[2]

  }];

  let description = [{
Outputs a vector with length `size` and the same dtype as `weights`. If
`weights` are empty, then index `i` stores the number of times the value `i` is
counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of
the value in `weights` at each index where the corresponding value in `arr` is
`i`.

Values in `arr` outside of the range [0, size) are ignored.

          },
          "structuredParameters": {

\n\nSo what happens? Decode first uses json or yaml to unmarshal the serialized data into your external MyAPIObject. That causes the raw JSON to be stored, but not unpacked. The next step is to copy (using pkg/conversion) into the internal struct. The runtime package's DefaultScheme has conversion functions installed which will unpack the JSON stored in RawExtension, turning it into the correct object type, and storing it in the Object. (TODO: In the case where the object is of an unknown type, a...

    arg is provided for better performance.
*   Adds a `save_traces` argument to `model.save`/ `tf.keras.models.save_model`
    which determines whether the SavedModel format stores the Keras model/layer
    call functions. The traced functions allow Keras to revive custom models and
    layers without the original class definition, but if this isn't required the