def LowerScatterNdOp :
  Pat<(TF_ScatterNdOp $indices,
       TensorOf<[AnyInteger, AnyFloat, AnyComplex]>:$updates, $shape, ConstantStrAttr<StrAttr, "">),
      (TF_TensorScatterAddOp
       (TF_FillOp $shape, (TF_ConstOp (GetScalarOfType<0> $updates))),
       $indices, $updates)>;

def LowerScatterNdOpDefaultBadIndicesPolicy :
  Pat<(TF_ScatterNdOp $indices,

  let description = [{
This operation computes

    # Scalar indices
    ref[indices, ...] = max(ref[indices, ...], updates[...])

    # Vector indices (for each i)
    ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])

    # High rank indices (for each i, ..., j)
    ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])

def LegalizeGather: Pat<
  (TF_GatherOp $params, $indices, $ignored_validate_indices),
  (TFL_GatherOp $params, $indices, ConstantAttr<I32Attr, "0">,
     ConstantAttr<I32Attr, "0">)>;

def LegalizeGatherNd : Pat<(TF_GatherNdOp $params, $indices, $bad_indices_policy),
                           (TFL_GatherNdOp $params, $indices)>;

def LegalizeGatherV2 : Pat<
  (TF_GatherV2Op $params, $indices, (Arith_ConstantOp ElementsAttr:$axis), $batch_dims),

          loc, indices, swap_index, scalar_one);

      // Then perform the swap.
      // indices[i] <- indices[swaps[i]]
      indices = builder->create<mhlo::DynamicUpdateSliceOp>(
          loc, indices.getType(), indices, target_index, llvm::ArrayRef(i));
      // indices[swaps[i]] <- indices[i]
      indices = builder->create<mhlo::DynamicUpdateSliceOp>(

    take value `on_value`, while all other locations take value `off_value`.

    If the input `indices` is rank `N`, the output will have rank `N+1`,
    The new axis is created at dimension `axis` (default: the new axis is
    appended at the end).
  }];

  let arguments = (ins
    TFL_TensorOf<[I32, I64]>:$indices,
    TFL_I32Tensor:$depth,

// Stores information about how to quantize a user-specified custom operation.
// CustomOpInfo contains info of its corresponding CustomOp registered in the
// CustomOpMap. 'quantizable_input_indices' is used to determine which indices
// of the CustomOp are quantizable. 'is_weight_only' is used specify whether the
// custom op is quantized only for storage and dequantized at runtime.
// 'no_side_effect' is used to determine whether the op can be pruned if

	if len(indices) == 0 {
		return errInvalidArgument
	}

	if z.SinglePool() {
		return errInvalidArgument
	}

	// Make pool unwritable before decommissioning.
	if err := z.StartDecommission(ctx, indices...); err != nil {
		return err
	}

	go func() {
		for _, idx := range indices {

		return nil
	}
	return mms.indices[mms.mustGetSingleMainModule()]
}

func (mms *MainModuleSet) Index(m module.Version) *modFileIndex {
	mms.indexMu.Lock()
	defer mms.indexMu.Unlock()
	return mms.indices[m]
}

func (mms *MainModuleSet) SetIndex(m module.Version, index *modFileIndex) {
	mms.indexMu.Lock()
	defer mms.indexMu.Unlock()
	mms.indices[m] = index
}

        // this loop by index is required to avoid possible ConcurrentModificationException
        val annotations = annotationContainer.resolvedCompilerRequiredAnnotations(firSymbol)
        for (index in annotations.indices) {
            val annotation = annotations[index]
            if (annotation.toAnnotationClassIdSafe(useSiteSession) == classId) {
                return true
            }
        }

        false

    for (const auto& phase : group.second) {
      for (const auto& pass : phase.second) {
        // Iterate over the pass_names_ and insert the pass pointer at all the
        // corresponding indices in the pass_ids vector.
        auto iter = pass_names.begin();
        while ((iter = std::find(iter, pass_names.end(), pass->name())) !=
               pass_names.end()) {