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:

		}

		fA, okAf := a.bytesToFloat()
		fB, okBf := b.bytesToFloat()
		if okAf && okBf {
			a.setFloat(fA)
			b.setFloat(fB)
			return nil
		}

		// Check if they int and float combination.
		if okAi && okBf {
			a.setInt(iA)
			b.setFloat(fA)
			return nil
		}
		if okBi && okAf {
			a.setFloat(fA)
			b.setInt(iB)
			return nil
		}

		// Not numeric types at this point.

		// 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

pkg reflect, method (Value) Set(Value)
pkg reflect, method (Value) SetBool(bool)
pkg reflect, method (Value) SetBytes([]uint8)
pkg reflect, method (Value) SetComplex(complex128)
pkg reflect, method (Value) SetFloat(float64)
pkg reflect, method (Value) SetInt(int64)
pkg reflect, method (Value) SetLen(int)
pkg reflect, method (Value) SetMapIndex(Value, Value)
pkg reflect, method (Value) SetPointer(unsafe.Pointer)