class_docstr: A dynamically quantized transposed convolution module with floating point tensors as inputs and outputs.
  
  For details on input arguments, parameters, and implementation see
  :class:`~torch.nn.ConvTranspose2d`.
  
  For special notes, please, see :class:`~torch.ao.nn.quantized.dynamic.Conv2d`
  
  Attributes:
      weight (Tensor):     packed tensor derived from the learnable weight
                           parameter.
      scale (Tensor):      scalar for the output scale
      zero_point (Tensor): scalar for the output zero point
  See :class:`~torch.nn.ConvTranspose2d` for other attributes.
  
  Examples::
  
      >>> # xdoctest: +SKIP
      >>> # With square kernels and equal stride
      >>> m = nnq.ConvTranspose2d(16, 33, 3, stride=2)
      >>> # non-square kernels and unequal stride and with padding
      >>> m = nnq.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
      >>> output = m(input)
      >>> # exact output size can be also specified as an argument
      >>> downsample = nnq.Conv2d(16, 16, 3, stride=2, padding=1)
      >>> upsample = nnq.ConvTranspose2d(16, 16, 3, stride=2, padding=1)
      >>> h = downsample(input)
      >>> h.size()
      torch.Size([1, 16, 6, 6])
      >>> output = upsample(h, output_size=input.size())
      >>> output.size()
      torch.Size([1, 16, 12, 12])
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code: def _get_name(self):
      return "DynamicQuantizedConvTranspose2d"
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch._ops import ops
  from torch.nn.common_types import _size_1_t
  from torch.nn.modules.utils import _single, _pair, _triple
  from torch.ao.nn.quantized.modules.conv import _reverse_repeat_padding
  import torch.ao.nn.quantized
  import warnings
label_desc: Use image node_id 2 for calling the ConvTranspose2d obj's underlying member method code with example usage: obj._get_name() and returns: str