code: def choose_qparams_tensor_meta(
      input: torch.Tensor,
      quant_min: int,
      quant_max: int,
      eps: float,
      dtype: torch.dtype,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      assert (
          input.dtype == torch.float32
      ), f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
      assert (
          quant_min < quant_max
      ), f"Expecting quant_min to be smaller than quant_max but received min: \
          {quant_min} max: {quant_max}"
      return torch.empty(
          1, dtype=torch.double, device=input.device
      ), torch.empty(1, dtype=torch.int64, device=input.device)
  
imports: import torch
  from torch.library import Library, impl
  from torch.ao.quantization.utils import determine_qparams, validate_qmin_qmax
  from typing import Tuple
label_desc: Use image node_id 14 for calling a global function with example usage: choose_qparams_tensor_meta(input, quant_min, quant_max, eps, dtype) and returns: torch,  torch