class_docstr: A placeholder for weight quantization parameters
class_docstr_tok: ["A","placeholder","for","weight","quantization","parameters"]
code: def __init__(self, weight, bias, scale, zero_point):
      self.weight = weight
      self.bias = None if bias is None else bias.detach().numpy()
      self.scale = _expr.const(scale)
      self.zero_point = _expr.const(zero_point, dtype="int32")
  
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 to create a new QNNParam object from inherited base classes: object with example: obj = QNNParam(weight, bias, scale, zero_point)