<< min_num_elements_for_weights_ << " elements).";
        call_op->removeAttr(kQuantTraitAttrName);
      }
    }

    StringRef function_name =
        mlir::cast<FlatSymbolRefAttr>(call_op.getFAttr()).getValue();
    if ((quantization_method_ == tensorflow::quantization::QuantizationMethod::
                                     METHOD_DYNAMIC_RANGE_INT8) &&
        (function_name.contains("batch_matmul") ||

    func::FuncOp ResolveFunc(::mlir::SymbolTableCollection* table) {
      if (table)
        return table->lookupNearestSymbolFrom<func::FuncOp>(*this, getFAttr());
      return SymbolTable::lookupNearestSymbolFrom<func::FuncOp>(*this, getFAttr());
    }
    // TODO(b/204997177): Deprecate and remove.
    func::FuncOp func() {  return ResolveFunc(nullptr); }
  }];
}

// WarningsOnUpdate returns warnings for the given update.
func (csrApprovalStrategy) WarningsOnUpdate(ctx context.Context, obj, old runtime.Object) []string {
	return nil
}

// GetAttrs returns labels and fields of a given object for filtering purposes.
func GetAttrs(obj runtime.Object) (labels.Set, fields.Set, error) {
	csr, ok := obj.(*certificates.CertificateSigningRequest)
	if !ok {
		return nil, nil, fmt.Errorf("not a certificatesigningrequest")

    auto range = llvm::make_filter_range(op->getUsers(), [&](Operation *user) {
      bool same_block = user->getBlock() == op->getBlock();
      bool same_device = op->getAttr(kDeviceAttr) == user->getAttr(kDeviceAttr);
      return same_block && same_device && state.IsMember(user);
    });
    return {range.begin(), range.end()};
  }

  // Users of block argument must be in the same block.

    for (const auto &idx_args : llvm::enumerate(new_block->getArguments())) {
      inner_op->setOperand(idx_args.index(), idx_args.value());
    }
    custom_op->setAttrs(inner_op->getAttrs());
    builder.create<YieldOp>(loc, inner_op->getResults());
    custom_op.getBody().takeBody(region);

    op->replaceAllUsesWith(custom_op);
    op->erase();
  }
}
}  // namespace

        op.getLoc(),
        /*result=*/
        mlir::cast<TensorType>(op.getResult().getType())
            .clone(builder.getI4Type()),
        /*operand=*/op.getOperands(), op->getAttrs());
    rewriter.replaceAllUsesWith(op.getResult(), new_gather_op.getResult());

    return success();
  }
};

class ReduceTypePrecisionPass

  // Clone this old replicate op but with new result types.
  auto new_replicate_op = builder.create<tf_device::ReplicateOp>(
      replicate_op->getLoc(), new_result_types, replicate_op->getOperands(),
      replicate_op->getAttrs());

  // Move region to the new op.
  new_replicate_op.getRegion().takeBody(replicate_op.getRegion());

  // Replace all old uses with new op results.
  int old_num_results = replicate_op->getNumResults();

void GroupByDialectPass::processFunction(
    mlir::func::FuncOp func, int& counter,
    llvm::SmallDenseSet<StringRef>& dialects, Operation* module) {
  // don't re-process functions we generated
  if (func->getAttr("dialect")) return;
  processRegion(func.getBody(), counter, dialects, module);
}

void GroupByDialectPass::processRegion(mlir::Region& region, int& counter,

    // OperationState so we can add regions to the new up.
    OperationState state(op->getLoc(), op->getName().getStringRef(), operands,
                         new_results, op->getAttrs(), op->getSuccessors());
    for (Region &region : op->getRegions()) {
      Region &new_region = *state.addRegion();
      rewriter.inlineRegionBefore(region, new_region, new_region.begin());

		TableConvertor: printerstorage.TableConvertor{TableGenerator: printers.NewTableGenerator().With(printersinternal.AddHandlers)},
	}
	options := &generic.StoreOptions{RESTOptions: optsGetter, AttrFunc: persistentvolume.GetAttrs}
	if err := store.CompleteWithOptions(options); err != nil {
		return nil, nil, err
	}

	statusStore := *store
	statusStore.UpdateStrategy = persistentvolume.StatusStrategy