"not a dictionary");

    auto calledComputations = op.getCalledComputations();
    if (!calledComputations || calledComputations.size() != 1)
      return op->emitError("expected exactly one called_computation");

    auto decomposition = mlir::cast<FlatSymbolRefAttr>(calledComputations[0]);

    auto composite = rewriter.create<mlir::stablehlo::CompositeOp>(

      return op.emitOpError() << "ApproxTopK takes an even number of operands.";
    }

    auto called_computations = op.getCalledComputations();
    if (called_computations.size() != 1) {
      return op.emitOpError()
             << "ApproxTopK takes exactly 1 called_computation.";
    }
    mlir::func::FuncOp callee = module_op_->lookupSymbol<mlir::func::FuncOp>(

  %0 = "vhlo.custom_call_v1" (%arg0, %arg1) <{api_version = #vhlo<api_version_v1 API_VERSION_ORIGINAL>, 
    backend_config = #vhlo.string_v1<"">,
    call_target_name = #vhlo.string_v1<"tf.ResizeBilinear">,
    called_computations = #vhlo.array_v1<[]>,
    has_side_effect = #vhlo.bool_v1<false>,
    operand_layouts = #vhlo.array_v1<[]>,
    output_operand_aliases = #vhlo.array_v1<[]>,

    %1:2 = stablehlo.custom_call @stablehlo.composite(%arg0, %arg1) {called_computations = [@foo.impl], composite.backend_config = {attributes = {bar = 500 : i64}, name = "odml.foo"}} : (tensor<1xf32>, tensor<2xf32>) -> (tensor<2xf32>, tensor<1xf32>)
    return
  }
  // CHECK-LABEL: func private @foo.impl

    attributes.emplace_back(builder.getNamedAttr(
        "backend_config", BuildVhloStringV1Attr(op->backend_config, builder)));
    attributes.emplace_back(builder.getNamedAttr(
        "called_computations", BuildVhloArrayV1Attr({}, builder)));
    attributes.emplace_back(builder.getNamedAttr(
        "operand_layouts", BuildVhloArrayV1Attr({}, builder)));
    attributes.emplace_back(builder.getNamedAttr(

  iota_dimension : long;
}

table StablehloCustomCallOptions {
  call_target_name : string;
  has_side_effect : bool;
  backend_config: string;
  api_version : int; // will be decprecated
  called_computations: [int]; // should point to subgraphs of the computations
  custom_attributes : [ubyte];
}

table StablehloReduceOptions {
  dimensions : [long];
  body_subgraph_index : int;
}

  { auto _e = api_version(); _o->api_version = _e; }
  { auto _e = called_computations(); if (_e) { _o->called_computations.resize(_e->size()); for (::flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->called_computations[_i] = _e->Get(_i); } } else { _o->called_computations.resize(0); } }

  %0:2 = mhlo.custom_call @ApproxTopK(%arg0, %arg1, %arg2, %arg3) {
    api_version = 4 : i32,
    called_computations = [@top_k_gt_f32_comparator],
    backend_config = {
      aggregate_to_topk = true,
      is_fallback = true,
      recall_target = 8.500000e-01 : f32,
      reduction_dim = 1 : i64,