class_docstr: A placeholder for quantized conv2d op attributes
  As of PyTorch 1.6, attributes of quantized conv2d ops, like
  stride, padding etc are stored in ConvPackedParams objects,
  together with weights and quantization parameters
class_docstr_tok: ["A","placeholder","for","quantized","conv2d","op","attributes","As","of","PyTorch","1.6",",","attributes","of","quantized","conv2d","ops",",","like","stride",",","padding","etc","are","stored","in","ConvPackedParams","objects",",","together","with","weights","and","quantization","parameters"]
code: def __init__(
      self,
      weight_np,
      bias,
      scale,
      zero_point,
      stride,
      padding,
      dilation,
      groups,
      output_padding,
  ):
      super().__init__(weight_np, bias, scale, zero_point)
      self.stride = stride
      self.padding = padding
      self.dilation = dilation
      self.groups = groups
      # Used only for conv_transpose2d
      self.output_padding = output_padding
  
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 ConvPackedParam object from inherited base classes: QNNParam with example: obj = ConvPackedParam(weight_np, bias, scale, zero_point, stride, padding, dilation, groups, output_padding)