TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_BiasAddOp : TF_Op<"BiasAdd", [Pure, TF_LayoutSensitiveInterface]> {
  let summary = "Adds `bias` to `value`.";

  let description = [{
This is a special case of `tf.add` where `bias` is restricted to be 1-D.
Broadcasting is supported, so `value` may have any number of dimensions.
  }];

  let arguments = (ins

        *   The new backend is used for 8 bits full integer post-training
            quantization
        *   The new backend removes the redundant rescales and fixes some bugs
            (shared weight/bias, extremely small scales, etc)
        *   Set `experimental_new_quantizer` in tf.lite.TFLiteConverter to False
            to disable this change
*   `tf.keras`
    *   `tf.keras.metrics.AUC` now support logit predictions.

		{"Timex.Stbcnt", Field, 0},
		{"Timex.Tai", Field, 0},
		{"Timex.Tick", Field, 0},
		{"Timex.Time", Field, 0},
		{"Timex.Tolerance", Field, 0},
		{"Timezoneinformation", Type, 0},
		{"Timezoneinformation.Bias", Field, 0},
		{"Timezoneinformation.DaylightBias", Field, 0},
		{"Timezoneinformation.DaylightDate", Field, 0},
		{"Timezoneinformation.DaylightName", Field, 0},
		{"Timezoneinformation.StandardBias", Field, 0},