}
    data_types.push_back(data_type);
  }
  n->ClearAttr(type_attr_name);
  n->AddAttr(type_attr_name, data_types);

  return data_types;
}

// Add edges from lifted outside compilation argument nodes to `n` in Graph `g`.
void AddEdgesFromOutsideCompilationNodes(
    const int original_arg_count, const int arg_to_input_edge_offset,
    const std::vector<DataType>& data_types,

          {{"outside_compilation_O1_host_compute"},
           "XlaHostCompute",
           {"C:o:0", "c:o:0"},
           {{"Tinputs", absl::Span<const DataType>({DT_FLOAT, DT_FLOAT})},
            {"Toutputs", absl::Span<const DataType>({DT_FLOAT})},
            {"ancestors", absl::Span<const string>({})},
            {"key", "host_compute_channel_F1_F1_O1"},
            {"send_key", ""},

    auto* context = shape_refiner_->GetContext(&node);
    DataType dtype = node.input_type(idx);
    return ConvertDataTypeAndShape(dtype, context->input(idx),
                                   context->input_handle_shapes_and_types(idx),
                                   context, builder);
  }
  DataType dtype = node.properties()->input_types[idx];
  mlir::Type element_type;

  desc->node_builder.Attr(attr_name, static_cast<DataType>(value));
}

void TF_SetAttrTypeList(TF_OperationDescription* desc, const char* attr_name,
                        const TF_DataType* values, int num_values) {
  desc->node_builder.Attr(
      attr_name, ArraySlice<const DataType>(
                     reinterpret_cast<const DataType*>(values), num_values));
}

    return op.emitOpError("number of segments should be a 0-D tensor");

  auto data_type = op.getData().getType().template dyn_cast<RankedTensorType>();
  auto segment_ids_type =
      op.getSegmentIds().getType().template dyn_cast<RankedTensorType>();
  if (data_type && segment_ids_type) {
    if (data_type.getRank() < segment_ids_type.getRank())
      return op.emitOpError(

  }
  os << "<unknown TFL type>";
}

Type TFLDialect::parseType(DialectAsmParser& parser) const {
  StringRef data_type;
  if (parser.parseKeyword(&data_type)) return Type();
  if (data_type == "control") return ControlType::get(getContext());
  parser.emitError(parser.getNameLoc()) << "unknown TFL type: " << data_type;
  return nullptr;
}

void TFLDialect::initialize() {
  addOperations<
#define GET_OP_LIST

","s","valueOrFunction","encodeURIComponent","serialize","serializeArray","r20","rhash","rantiCache","rheaders","rnoContent","rprotocol","transports","allTypes","originAnchor","addToPrefiltersOrTransports","structure","dataTypeExpression","dataType","dataTypes","inspectPrefiltersOrTransports","jqXHR","inspected","seekingTransport","inspect","prefilterOrFactory","dataTypeOrTransport","ajaxExtend","flatOptions","ajaxSettings","active","lastModified","etag","url","isLocal","protocol","processData",...

","s","valueOrFunction","encodeURIComponent","serialize","serializeArray","r20","rhash","rantiCache","rheaders","rnoContent","rprotocol","transports","allTypes","originAnchor","addToPrefiltersOrTransports","structure","dataTypeExpression","dataType","dataTypes","inspectPrefiltersOrTransports","jqXHR","inspected","seekingTransport","inspect","prefilterOrFactory","dataTypeOrTransport","ajaxExtend","flatOptions","ajaxSettings","active","lastModified","etag","url","isLocal","protocol","processData",...

  // trouble calling ConvertToTensor(). For now, extract the tensor data from
  // ElementsAttr directly in this and read type from tflite::TensorType instead
  // of tensorflow::DataType.
  auto type = mlir::cast<TensorType>(value.getType());
  tflite::TensorType tflite_element_type =
      GetTFLiteType(type.getElementType()).value();
  if (tflite_element_type == tflite::TensorType_INT4) {

  func.func @unsupported_operand_type(%arg0: tensor<?xi2>) {
    // expected-error@+1 {{failed to determine operand type at index 0: Converting i2 to DataType}}