class_docstr: Quantized 3D Conv
code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      bias=True,
      padding_mode="zeros",
      **kwargs,
  ):
      kernel_size = _triple(kernel_size)
      stride = _triple(stride)
      padding = _triple(padding)
      dilation = _triple(dilation)
      (
          quant_desc_input,
          quant_desc_weight,
      ) = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs)
      super(QuantConv3d, self).__init__(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          padding,
          dilation,
          False,
          _triple(0),
          groups,
          bias,
          padding_mode,
          quant_desc_input=quant_desc_input,
          quant_desc_weight=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 lightseq.training.pytorch_quantization import tensor_quant
  from .None import _utils
label_desc: Use image node_id 1 to create a new QuantConv3d object from inherited base classes: _QuantConvNd with example: obj = QuantConv3d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode)