code: def _get_quant_params(qweight):
      import torch
  
      weight_np = qweight.dequantize().numpy()
  
      if qweight.qscheme() == torch.per_tensor_affine:
          return (
              weight_np,
              qweight.q_scale(),
              int(qweight.q_zero_point()),
          )
  
      scales = qweight.q_per_channel_scales().numpy()
      zero_points = qweight.q_per_channel_zero_points().numpy()
      # This is an assumption posed by QNN
      msg = (
          "The values of zero points should be all zero for per channel"
      )
      assert np.all(zero_points == 0), msg
      return weight_np, scales, 0
  
imports: import numpy
  import tvm
  from tvm import relay
  from tvm.relay import expr
  from tvm.relay import op
  from tvm.relay.frontend.common import infer_shape
  from .common import logger
  from .pytorch_utils import is_version_greater_than
label_desc: Use image node_id 1 for calling a global function with example usage: _get_quant_params(qweight) and returns: weight_np,  scales,  int, weight_np,  qweight,  int