fi
  ${im_cmd} -thumbnail ${image_size} ${tmp_png_file} "${output_file}"
  rm -f ${tmp_png_prefix}*png
elif [[ x"${cmd_type}" = "xsvg" ]] ; then
  check_command rsvg-convert
  target_file=$(echo "$url" | sed -e "s#^file:/*#/#g")
  rsvg-convert -w 100 -h 100 --keep-aspect-ratio "${target_file}" -o "${output_file}"
elif [[ x"${cmd_type}" = "ximage" ]] ; then
  im_cmd=$(get_imagemagick_cmd)
  if [[ -z "${im_cmd}" ]] ; then

        dependant.request_param_name = param_name
        return True
    elif lenient_issubclass(type_annotation, WebSocket):
        dependant.websocket_param_name = param_name
        return True
    elif lenient_issubclass(type_annotation, HTTPConnection):
        dependant.http_connection_param_name = param_name
        return True
    elif lenient_issubclass(type_annotation, Response):

    tags_schema = {"default": [], "title": "Tags"}
    if mod_name.startswith("tutorial001"):
        tags_schema.update(UNTYPED_LIST_SCHEMA)
    elif mod_name.startswith("tutorial002"):
        tags_schema.update(LIST_OF_STR_SCHEMA)
    elif mod_name.startswith("tutorial003"):
        tags_schema.update(SET_OF_STR_SCHEMA)

    response = client.get("/openapi.json")

            resolve_file(item=item, files=files, config=config)
        elif isinstance(item, dict):
            assert len(item) == 1
            values = list(item.values())
            if not values:
                continue
            if isinstance(values[0], str):
                resolve_file(item=values[0], files=files, config=config)
            elif isinstance(values[0], list):

        elif pr.state == "closed" or approved_label in label_strs:
            logging.info(f"Already approved or closed PR #{pr.number}")
            if already_done_comment:
                logging.info(
                    f"This PR #{pr.number} was already marked as done in comment: {already_done_comment.url}"
                )
            elif already_notified_comment:

fi

# Execute pre-build script based on BUILD_TYPE_ID
if [[ "${BUILD_TYPE_ID:-}" == Gradle_Xperimental* ]]; then
  execute_build_script_from_env "${XPERIMENTAL_EC2_PRE_BUILD_SCRIPT:-}"
elif [[ "${BUILD_TYPE_ID:-}" == Gradle_Master* ]]; then
  execute_build_script_from_env "${MASTER_EC2_PRE_BUILD_SCRIPT:-}"
fi

# READ-ONLY DEPENDENCY CACHE
if [ -d "/opt/gradle-cache" ]; then

            example = getattr(field_info, "example", None)
            if openapi_examples:
                parameter["examples"] = jsonable_encoder(openapi_examples)
            elif example != Undefined:
                parameter["example"] = jsonable_encoder(example)
            if getattr(field_info, "deprecated", None):
                parameter["deprecated"] = True

            if code_block4_pattern.match(line):
                in_code_block4 = True
            elif code_block3_pattern.match(line):
                in_code_block3 = True
        else:
            if in_code_block4 and code_block4_pattern.match(line):
                in_code_block4 = False
            elif in_code_block3 and code_block3_pattern.match(line):
                in_code_block3 = False

        if (
            key in main_dict
            and isinstance(main_dict[key], dict)
            and isinstance(value, dict)
        ):
            deep_dict_update(main_dict[key], value)
        elif (
            key in main_dict
            and isinstance(main_dict[key], list)
            and isinstance(update_dict[key], list)
        ):
            main_dict[key] = main_dict[key] + update_dict[key]

        for arg in get_args(annotation):
            if field_annotation_is_scalar_sequence(arg):
                at_least_one_scalar_sequence = True
                continue
            elif not field_annotation_is_scalar(arg):
                return False
        return at_least_one_scalar_sequence
    return field_annotation_is_sequence(annotation) and all(
        field_annotation_is_scalar(sub_annotation)