class_docstr: base class of quantized Conv inherited from _ConvNd
  
  Comments of original arguments can be found in torch.nn.modules.conv
  
  Arguments:
      quant_desc_input: An instance of :class:`QuantDescriptor <csrc.pytorch.pytorch_quantization.tensor_quant.QuantDescriptor>`.
          Quantization descriptor of input.
      quant_desc_weight: An instance of :class:`QuantDescriptor <csrc.pytorch.pytorch_quantization.tensor_quant.QuantDescriptor>`.
          Quantization descriptor of weight.
  
  Raises:
      ValueError: If unsupported arguments are passed in.
  
  Readonly properties:
      - input_quantizer:
      - weight_quantizer:
  
  Static methods:
      - set_default_quant_desc_input: Set default_quant_desc_input
      - set_default_quant_desc_weight: Set default_quant_desc_weight
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code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride,
      padding,
      dilation,
      transposed,
      output_padding,
      groups,
      bias,
      padding_mode,
      quant_desc_input,
      quant_desc_weight,
  ):
      super(_QuantConvNd, self).__init__(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          padding,
          dilation,
          transposed,
          output_padding,
          groups,
          bias,
          padding_mode,
      )
      self.init_quantizer(quant_desc_input, quant_desc_weight)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.modules.utils import _single, _pair, _triple
  from torch.nn.modules.conv import _ConvTransposeNd
  from csrc.pytorch.pytorch_quantization import tensor_quant
  from .None import _utils
label_desc: Use image node_id 1 to create a new _QuantConvNd object from inherited base classes: torch.nn.modules.conv, _utils with example: obj = _QuantConvNd(in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode, quant_desc_input, quant_desc_weight)