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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def transpose(self, name=None):
 """Returns matching `Conv2DTranspose` module. Args: name: Optional string assigning name of transpose module. The default name is constructed by appending "_transpose" to `self.name`. Returns: `Conv2DTranspose` module. Raises: base.NotSupportedError: If `rate` in any dimension > 1. """

    if any(x > 1 for x in self._rate):
      raise base.NotSupportedError(
          "Cannot transpose a dilated convolution module.")

    if any(p != self._conv_op_padding for p in self._padding):
      raise base.NotSupportedError(
          "Cannot tranpose a convolution using mixed paddings or paddings "
          "other than SAME or VALID.")

    if name is None:
      name = self.module_name + "_transpose"

    def output_shape():
      if self._data_format == DATA_FORMAT_NCHW:
        return self.input_shape[2:4]
      else:  # data_format == DATA_FORMAT_NHWC
        return self.input_shape[1:3]

    return Conv2DTranspose(output_channels=lambda: self._input_channels,
                           output_shape=output_shape,
                           kernel_shape=self._kernel_shape,
                           stride=self._stride,
                           padding=self._conv_op_padding,
                           use_bias=self._use_bias,
                           initializers=self._initializers,
                           partitioners=self._partitioners,
                           regularizers=self._regularizers,
                           data_format=self._data_format,
                           custom_getter=self._custom_getter,
                           name=name)