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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 _new_convolution(self, use_bias):
 """Returns new convolution. Args: use_bias: Use bias in convolutions. If False, clean_dict removes bias entries from initializers, partitioners and regularizers passed to the constructor of the convolution. """

    def clean_dict(input_dict):
      if input_dict and not use_bias:
        cleaned_dict = input_dict.copy()
        cleaned_dict.pop("b", None)
        return cleaned_dict
      return input_dict
    return self._conv_class(
        output_channels=4*self._output_channels,
        kernel_shape=self._kernel_shape,
        stride=self._stride,
        rate=self._rate,
        padding=self._padding,
        use_bias=use_bias,
        initializers=clean_dict(self._initializers),
        partitioners=clean_dict(self._partitioners),
        regularizers=clean_dict(self._regularizers),
        name="conv")