class_docstr: Quantized ConvTranspose1d
code: def forward(self, input, output_size=None):
      if self.padding_mode != "zeros":
          raise ValueError(
              "Only `zeros` padding mode is supported for QuantConvTranspose1d"
          )
  
      output_padding = self._output_padding(
          input,
          output_size,
          self.stride,
          self.padding,
          self.kernel_size,
      )
  
      quant_input, quant_weight = self._quant(input)
      output = F.conv_transpose1d(
          quant_input,
          quant_weight,
          self.bias,
          self.stride,
          self.padding,
          output_padding,
          self.groups,
          self.dilation,
      )
      return output
  
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 2 for calling the QuantConvTranspose1d obj's underlying member method code with example usage: obj.forward(input, output_size) and returns: output