case int:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case int8:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case int16:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case int32:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case uint:
				field.ReflectValueOf(ctx, value).SetFloat(float64(data))
			case uint8:

		// inferring untyped values are numbers.
		if !argVal.isNumeric() {
			if i, ok := argVal.bytesToInt(); ok {
				argVal.setInt(i)
			} else if f, ok := argVal.bytesToFloat(); ok {
				argVal.setFloat(f)
			} else {
				return errNonNumericArg(funcName)
			}
		}
	}

	// Mark that we have seen one non-null value.
	isFirstRow := false
	if !e.aggregate.seen {
		e.aggregate.seen = true

        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with
        `tf.keras.backend.set_floatx('float64')`, or pass `dtype='float64'` to
        each of the Layer constructors. See `tf.keras.layers.Layer` for more
        information.
    *   Some `tf.assert_*` methods now raise assertions at operation creation