if c {
		i = 1
	}
	return f.constVal(OpConstBool, t, i, true)
}
func (f *Func) ConstInt8(t *types.Type, c int8) *Value {
	return f.constVal(OpConst8, t, int64(c), true)
}
func (f *Func) ConstInt16(t *types.Type, c int16) *Value {
	return f.constVal(OpConst16, t, int64(c), true)
}
func (f *Func) ConstInt32(t *types.Type, c int32) *Value {
	return f.constVal(OpConst32, t, int64(c), true)
}

	return s.f.ConstBool(types.Types[types.TBOOL], c)
}
func (s *state) constInt8(t *types.Type, c int8) *ssa.Value {
	return s.f.ConstInt8(t, c)
}
func (s *state) constInt16(t *types.Type, c int16) *ssa.Value {
	return s.f.ConstInt16(t, c)
}
func (s *state) constInt32(t *types.Type, c int32) *ssa.Value {
	return s.f.ConstInt32(t, c)
}
func (s *state) constInt64(t *types.Type, c int64) *ssa.Value {

// CHECK:  %[[ADD2:.*]] = "tf.Add"(%[[MUL2]], %[[SUB]])
// CHECK:  %[[BATCHNORM1_a:[^,]+]], {{.*}} = "tf.FusedBatchNormV3"(%[[ADD2]], %[[CONSTANT1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
// CHECK:  %[[BATCHNORM1_b:[^,]+]], {{.*}} = "tf.FusedBatchNormV3"(%[[BATCHNORM1_a]], %[[CONSTANT1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
// CHECK:  "tf.FusedBatchNormV3"(%[[BATCHNORM1_b]], %[[ARG1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
}

  // CHECK: func private @_stablehlo_main_1(%arg0: tensor<i32>) -> tensor<1024x3xf32> attributes {_from_xla_call_module}
  // CHECK: %[[CONSTANT1:.*]] = stablehlo.constant dense<1.000000e+03> : tensor<1024x3xf32>
  // CHECK: return %[[CONSTANT1:.*]]
  // CHECK: }

  // CHECK: func private @_stablehlo_main_0(%arg0: tensor<i32>
  // CHECK-SAME: %[[INPUT1:.*]]: tensor<1024x3xf32>, %[[INPUT2:.*]]: tensor<1024x3xf32>

  // CHECK:   [[NEW_IV:%.*]] = chlo.broadcast_add [[ITER_ARG0]], [[ONE]]
  // CHECK:   mhlo.return [[NEW_IV]], [[ITER_ARG1]], [[INDICES2]]
  // CHECK: }

  // CHECK: [[CONSTANT1:%.*]] = mhlo.constant dense<1> : tensor<1xi64>
  // CHECK: [[ARITH_CONSTANT:%.*]] = arith.constant 1 : index
  // CHECK: [[SHAPE_DIM:%.*]] = shape.dim %arg0, [[ARITH_CONSTANT]] : tensor<4x?x16xf32>, index -> index