strings::StrAppend(out, "  }\n\n");

  for (int i = 0; i < op_info.graph_op_def.output_arg_size(); ++i) {
    const string arg_range = strings::StrCat(
        "_outputs_range[\"", op_info.graph_op_def.output_arg(i).name(), "\"]");
    if (op_info.is_list_output[i]) {
      strings::StrAppend(out, "  for (int32 i = ", arg_range, ".first; i < ",
                         arg_range, ".second; ++i)\n");

    const OpRegistrationData* op_reg;
    if (OpRegistry::Global()->LookUp(call_target.name(), &op_reg).ok()) {
      const OpDef& op = op_reg->op_def;
      if (absl::c_any_of(op.output_arg(), [](const OpDef::ArgDef arg) {
            return arg.is_ref();
          })) {
        return true;
      }
      continue;
    }

    callee_ref_nodes.clear();

  for (int i = 0; i < graph_op_def.output_arg_size(); ++i) {
    // ApiDef arguments must be in the same order as in OpDef since
    // we initialize ApiDef based on OpDef.
    const auto& arg = graph_op_def.output_arg(i);
    const auto& api_def_arg(api_def.out_arg(i));
    CHECK_EQ(arg.name(), api_def_arg.name());

    bool is_list = ArgIsList(arg);
    output_types.push_back(

    TestUnavailableErrorOp);

string FunctionWithErrorOp(const tensorflow::StringPiece op_name) {
  const std::string& func_str =
      "    signature {"
      "      name: 'FunctionWith__OP_NAME__'"
      "      output_arg {"
      "        name: 'out'"
      "        type: DT_STRING"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'error_op'"
      "      op: '__OP_NAME__'"
      "    }"

          name: "num_shards"
          type: DT_INT32
        }
        output_arg {
          name: "filename"
          type: DT_STRING
        }
      }
      op {
        name: "StatefulPartitionedCall"
        input_arg {
          name: "args"
          type_list_attr: "Tin"
        }
        output_arg {
          name: "output"
          type_list_attr: "Tout"
        }

    const OpDef& signature = fdef.signature();
    ASSERT_EQ(outputs.size(), signature.output_arg_size());
    for (int i = 0; i < outputs.size(); ++i) {
      const OpDef::ArgDef& arg = signature.output_arg(i);
      const IOSpec& out = outputs[i];
      if (out.second != DT_INVALID) {
        ASSERT_EQ(arg.type(), out.second)
            << "Got unexpected type for output " << i

  def tpu_subgraph():
      return tf.tpu.rewrite(model_fn, args)

  return tf.raw_ops.TPUPartitionedCall(
      args=tpu_subgraph.captured_inputs,
      Tout=[o.type for o in tpu_subgraph.definition.signature.output_arg],
      f=tpu_subgraph,
      device_ordinal=[0])

static_shape = tf.placeholder(tf.int32, shape=([3]), name='static_size')

w = tf.Variable(tf.constant([[1.0], [2.0], [3.0]]), name='w')

        when:
        relocatedJarCreator.create(outputJar, [jarFile1, jarFile2, jarFile3, jarFile4, jarFile5, jarFile6, inputDirectory])

        then:

        TestFile[] contents = tmpDir.testDirectory.listFiles().findAll { it.isFile() }
        contents.length == 1
        contents[0] == outputJar
        handleAsJarFile(outputJar) { JarFile file ->
            List<JarEntry> entries = file.entries() as List

const std::vector<ArgView>& OpView::Outputs() const { return output_args_; }

const std::vector<AttrView>& OpView::Attributes() const {
  return argument_attrs_;
}

const std::vector<OpArgumentView>& OpView::AllArguments() const {
  return all_arguments_;
}

int OpView::NumInputs() const { return input_args_.size(); }

int OpView::NumOutputs() const { return output_args_.size(); }

ArgView OpView::OnlyInput() const {

        List<String> sourceArgs = getSourceArgs(sourceFile);
        List<String> outputArgs = getOutputArgs(spec, getOutputFileDir(sourceFile, objectDir, objectFileExtension));
        List<String> pchArgs = maybeGetPCHArgs(spec, sourceFile);

        return newInvocation("compiling ".concat(sourceFile.getName()), objectDir, buildPerFileArgs(genericArgs, sourceArgs, outputArgs, pchArgs), spec.getOperationLogger());
    }