strides: Sequence[int] = (1, 1, 1, 1),
      dilations: Sequence[int] = (1, 1, 1, 1),
      padding: str = 'SAME',
      has_func_alias: bool = False,
  ) -> module.Module:
    class ConvModel(module.Module):
      """A simple model with a single conv2d, bias and relu."""

      def __init__(self):
        self.out_channel_size = filter_shape[-1]

      activation_fn: Optional[ops.Operation] = None,
      strides: Sequence[int] = (1, 2, 2, 1),
      dilations: Sequence[int] = (1, 1, 1, 1),
      padding: str = 'SAME',
  ):
    class ConvModel(module.Module):
      """A simple model with a single conv2d, bias and relu."""

      def __init__(self):
        self.out_channel_size = filter_shape[-1]

  def test_qat_conv_model(
      self,
      activation_fn: Optional[ops.Operation],
      has_bias: bool,
      has_batch_norm: bool,
      target_opset: quant_opts_pb2.OpSet,
  ):
    class ConvModel(module.Module):

      def __init__(self):
        self.filter_value = np.random.uniform(
            low=-0.5, high=0.5, size=(2, 3, 3, 2)
        ).astype('f4')

      @def_function.function(