// CHECK: [[VAL11:%.+]] = "tf.Mul"([[VAL10]]#1, [[VAL10]]#2)
  // CHECK: [[VAL12:%.+]] = "tf.ExpandDims"([[VAL10]]#0, [[VAL7]])
  // CHECK: [[VAL13:%.+]] = "tf.ExpandDims"([[VAL10]]#3, [[VAL7]])
  // CHECK: [[VAL14:%.+]] = "tf.ConcatV2"([[VAL12]], [[VAL3]], [[VAL3]], [[VAL13]], [[VAL7]])
  // CHECK: [[VAL15:%.+]] = "tf.Cast"([[VAL10]]#1)
  // CHECK: [[VAL16:%.+]] = "tf.Div"([[VAL15]], [[VAL6]])

				"has(self.val1)":                                      1,
				"has(self.val1) && has(self.val2)":                    2,
				"!has(self.val1)":                                     2,
				"self.val1.all(k, size(self.val1) > 0)":               7340028,
				"self.val1.exists_one(k, self.val1 == ['a'])":         15728629,
				"!self.val1.exists_one(k, size(self.val1) > 0)":       6291455,

				"!self.val1.contains('gives')",
				"self.val1.endsWith('👑')",
				"!self.val1.endsWith('pawn')",
				"self.val1.matches('^[^0-9]*$')",
				"!self.val1.matches('^[0-9]*$')",
				"type(self.val1) == string",
				"size(self.val1) == 12",

				// string functions (https://github.com/google/cel-go/blob/v0.9.0/ext/strings.go)
				"self.val1.charAt(3) == 'k'",
				"self.val1.indexOf('o') == 1",
				"self.val1.indexOf('o', 2) == 2",

# CHECK:           %[[VAL_9:.*]] = "tfl.transpose"(%[[VAL_1]], %[[VAL_2]]) : (tensor<3x7xf32>, tensor<2xi32>) -> tensor<7x3xf32>
# CHECK:           %[[VAL_10:.*]] = "tfl.fully_connected"(%[[VAL_7]]#0, %[[VAL_9]], %[[VAL_3]]) <{fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"}> : (tensor<1x5x3xf32>, tensor<7x3xf32>, none) -> tensor<5x7xf32>

    func.return %0 : tensor<1xf32>
  }

// CHECK:   func @simpleTest(%[[VAL_0:.*]]: tensor<1xf32>, %[[VAL_1:.*]]: tensor<1xf32>, %[[VAL_2:.*]]: tensor<1xf32>, %[[VAL_3:.*]]: tensor<1xf32>) -> tensor<2x1xf32> {
// CHECK:           %[[VAL_4:.*]] = call @func_0_GPU_FLOAT(%[[VAL_0]], %[[VAL_1]], %[[VAL_2]]) {tac.interface_name = "func_0"} : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

// CHECK:           [[VAL_6:%.*]] = "tfl.slice"([[VAL_1]], [[VAL_2]], [[VAL_5]]) : (tensor<1x8x8x1024xf32>, tensor<4xi32>, tensor<4xi32>) -> tensor<1x8x8x256xf32>
// CHECK:           [[VAL_7:%.*]] = "tfl.slice"([[VAL_1]], [[VAL_3]], [[VAL_5]]) : (tensor<1x8x8x1024xf32>, tensor<4xi32>, tensor<4xi32>) -> tensor<1x8x8x256xf32>

// CANON:             [[VAL_16:%.*]] = "tf.AddV2"([[VAL_11]], [[VAL_4]])
// CANON:             "tfl.yield"([[VAL_16]], [[VAL_14]], [[VAL_15]]) : (tensor<*xi32>, tensor<*xi32>, tensor<*xf32>) -> ()
// CANON:           }) : (tensor<i32>, tensor<i32>, tensor<?x256x256xf32>) -> (tensor<i32>, tensor<i32>, tensor<?x256x256xf32>)
// CANON:           return [[VAL_17:%.*]]#1, [[VAL_1]], [[VAL_17]]#2 : tensor<i32>, tensor<256x256xf32>, tensor<?x256x256xf32>

// PARTIAL:           %[[VAL_10:.*]] = "tfl.pseudo_const"() <{value = dense<0> : tensor<3xi32>}> : () -> tensor<3xi32>
// PARTIAL:           %[[VAL_11:.*]] = "tfl.slice"(%[[VAL_6]], %[[VAL_10]], %[[VAL_9]]) : (tensor<4x384x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x384x32xf32>
// PARTIAL:           return %[[VAL_11]] : tensor<1x384x32xf32>
// PARTIAL:         }

}

// -----

module {

            i1: va11
`,
		},
		{
			desc:     "partial create",
			baseYAML: testTreeYAML,
			path:     "a.b.d",
			value:    "val3",
			want: `
a:
  b:
    c: val1
    d: val3
    list1:
    - i1: val1
    - i2: val2
    - i3a: key1
      i3b:
        list2:
        - i1: val1
        - i2: val2
        - i3a: key1
          i3b:
            i1: va11
`,
		},

// CHECK-SAME:                                 %[[VAL_1:.*]]: tensor<3xf16>) -> tensor<2x3xf16> {
// CHECK:           %[[VAL_2:.*]] = "tf.Div"(%[[VAL_0]], %[[VAL_1]]) : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>
// CHECK:           %[[VAL_3:.*]] = "tf.Div"(%[[VAL_0]], %[[VAL_1]]) : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>
// CHECK:           %[[VAL_4:.*]] = "tf.FloorDiv"(%[[VAL_0]], %[[VAL_1]]) : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>