tf.constant(0, dtype=dtype))]

  expected = tf.constant([0, 0, 0, 0], dtype=tf.float32)
  tf.assert_equal(tf.cast(not_a_and_a, tf.float32), expected)

  expected = tf.cast([not_0] * 4, tf.float32)
  tf.assert_equal(tf.cast(not_a_or_a, tf.float32), expected)

  # For unsigned dtypes let's also check the result directly.
  if dtype.is_unsigned:

	A3 /* ERROR "invalid recursive type" */ [10]struct {
		x A4
	}
	A4 [10]A3

	F1 func()
	F2 func(x, y, z float32)
	F3 func(x, y, x /* ERROR "redeclared" */ float32)
	F4 func() (x, y, x /* ERROR "redeclared" */ float32)
	F5 func(x int) (x /* ERROR "redeclared" */ float32)
	F6 func(x ...int)

	I1 interface{}
	I2 interface {
		m1()
	}
	I3 interface {
		m1()

	// maps
	_ = make /* ERROR "arguments" */ (map[int]string, 10, 20)
	_ = make(map[int]float32, int /* ERROR "not an expression" */)
	_ = make(map[int]float32, "foo" /* ERROR "cannot convert" */)
	_ = make(map[int]float32, 10)
	_ = make(map[int]float32, n)
	_ = make(map[int]float32, int64(n))
	_ = make(map[string]bool, 10.0)
	_ = make(map[string]bool, 10.0<<s)

// Float32bits(Float32frombits(x)) == x.
func Float32bits(f float32) uint32 { return *(*uint32)(unsafe.Pointer(&f)) }

// Float32frombits returns the floating-point number corresponding
// to the IEEE 754 binary representation b, with the sign bit of b
// and the result in the same bit position.
// Float32frombits(Float32bits(x)) == x.
func Float32frombits(b uint32) float32 { return *(*float32)(unsafe.Pointer(&b)) }

      // If the tensor is a weight, it should have type INT8.
      // If the tensor is a bias, it should have type FLOAT32.
      // If the tensor is an input or output it should have type FLOAT32.
      // The input to dequantize should be INT8, and all other tensors should be
      // FLOAT32.
      if (i == dequant_input_idx) {
        EXPECT_EQ(quant_tensor->type(), TensorType_INT8);

	case nil:
		var v bool = t /* ERRORx `cannot use .* in variable declaration` */
		_ = v
	case int:
		var v int = t
		_ = v
	case float32, complex64:
		var v float32 = t /* ERRORx `cannot use .* in variable declaration` */
		_ = v
	default:
		var v float32 = t /* ERRORx `cannot use .* in variable declaration` */
		_ = v
	}

	var t I
	switch t.(type) {
	case T:

		}
		return math.MaxInt64, Below
	}

	panic("unreachable")
}

// Float32 returns the float32 value nearest to x. If x is too small to be
// represented by a float32 (|x| < [math.SmallestNonzeroFloat32]), the result
// is (0, [Below]) or (-0, [Above]), respectively, depending on the sign of x.
// If x is too large to be represented by a float32 (|x| > [math.MaxFloat32]),

	return *(*int64)(p) == *(*int64)(q)
}
func memequal128(p, q unsafe.Pointer) bool {
	return *(*[2]int64)(p) == *(*[2]int64)(q)
}
func f32equal(p, q unsafe.Pointer) bool {
	return *(*float32)(p) == *(*float32)(q)
}
func f64equal(p, q unsafe.Pointer) bool {
	return *(*float64)(p) == *(*float64)(q)
}
func c64equal(p, q unsafe.Pointer) bool {
	return *(*complex64)(p) == *(*complex64)(q)
}

// to the `output_buffer`. Both `model_buffer` and `output_buffer` should be a
// valid FlatBuffer format for Model supported by TFLite.
//
// The `input_type`, `output_type` and `inference_type` can be float32 / qint8 /
// int8 / int16.
//
// Returns a partially quantized model if `fully_quantize` is false. Returns a
// non-OK status if the quantization fails.
//

	// float
	{types.TFLOAT64, types.TFLOAT32}: {ssa.OpCvt64Fto32F, ssa.OpCopy, types.TFLOAT32},
	{types.TFLOAT64, types.TFLOAT64}: {ssa.OpRound64F, ssa.OpCopy, types.TFLOAT64},
	{types.TFLOAT32, types.TFLOAT32}: {ssa.OpRound32F, ssa.OpCopy, types.TFLOAT32},
	{types.TFLOAT32, types.TFLOAT64}: {ssa.OpCvt32Fto64F, ssa.OpCopy, types.TFLOAT64},
}