code: def _quantized_conv_transpose2d(with_relu=False):
      def _impl(inputs, _):
          # Refer to aten/src/ATen/native/quantized/cpu/qconv.cpp
          # Supported in Torch 1.7 or newer
          conv_params = inputs[1]
          weight = conv_params[0]
          weight_scale = conv_params[1]
          weight_zero_point = conv_params[2]
          bias = conv_params[3]
  
          strides = conv_params[4]
          padding = conv_params[5]
          dilation = conv_params[6]
          groups = conv_params[7]
          output_padding = conv_params[8]
  
          output_scale = _expr.const(inputs[2])
          output_zero_point = _expr.const(inputs[3])
  
          assert (
              len(inputs) == 6
          ), "Input quant params not found in op inputs"
  
          # These are manually added by add_input_quant_params_to_op_inputs above
          # In torch, they are retrieved from QTensor data structure at runtime
          input_scale = _expr.const(inputs[4])
          input_zero_point = _expr.const(inputs[5])
  
          weight_shape = list(infer_shape(weight))
  
          kernel_size = (weight_shape[2], weight_shape[3])
          out_channels = weight_shape[1]
  
          conv_out = relay.qnn.op.conv2d_transpose(
              inputs[0],
              weight,
              input_zero_point,
              weight_zero_point,
              input_scale,
              weight_scale,
              kernel_size=kernel_size,
              dilation=dilation,
              strides=strides,
              padding=padding,
              groups=groups,
              channels=out_channels,
              output_padding=output_padding,
              out_dtype="int32",
              kernel_layout="IOHW",
          )
  
          return _do_bias_and_requantize(
              conv_out,
              bias,
              input_scale,
              weight_scale,
              output_scale,
              output_zero_point,
              with_relu,
          )
  
      return _impl
  
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 35 for calling a global function with example usage: _quantized_conv_transpose2d(with_relu) and returns: _impl, _do_bias_and_requantize