def METHOD_NAME(param_val, param_desc: Optional[str]):
    if isinstance(param_val, list):
        # Like str(list), but avoid the whitespace padding.
        val_str = "[" + ",".join(str(x) for x in param_val) + "]"
        need_prefix_separator = False
    elif isinstance(param_val, bool):
        if param_val:
            val_str = "T"
        else:
            val_str = "F"
        need_prefix_separator = True
    elif isinstance(param_val, TensorContentConstant):
        val_str = f"const[{param_val.elem_value}]"
        need_prefix_separator = True
    elif isinstance(param_val, TensorContentDtypeMin):
        val_str = "min"
        need_prefix_separator = True
    elif isinstance(param_val, TensorContentDtypeMax):
        val_str = "max"
        need_prefix_separator = True
    elif isinstance(param_val, TensorContentRandom):
        val_str = "random"
        need_prefix_separator = True
    elif isinstance(param_val, TensorContentSequentialCOrder):
        val_str = f"seqC[start:{param_val.start_value},inc:{param_val.increment}]"
        need_prefix_separator = True
    else:
        val_str = str(param_val)
        need_prefix_separator = True
    if param_desc and need_prefix_separator:
        return f"{param_desc}:{val_str}"
    elif param_desc and not need_prefix_separator:
        return f"{param_desc}{val_str}"
    else:
        return val_str