code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      padding=0,
      output_padding=0,
      groups=1,
      bias=True,
      dilation=1,
      padding_mode="zeros",
      device=None,
      dtype=None,
      weight_qparams: Optional[Dict[str, Any]] = None,
  ):
      nn.ConvTranspose2d.__init__(
          self,
          in_channels,
          out_channels,
          kernel_size,
          stride,
          padding,
          output_padding,
          groups,
          bias,
          dilation,
          padding_mode,
          device,
          dtype,
      )
      self._init_weight_qparams(weight_qparams, device)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from typing import Optional, Dict, Any, List
  from torch.nn.common_types import _size_1_t
  from .utils import ReferenceQuantizedModule
label_desc: Use image node_id 1 to create a new ConvTranspose2d object from inherited base classes: _ConvTransposeNd, nn with example: obj = ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, output_padding, groups, bias, dilation, padding_mode, device, dtype, weight_qparams)