%tl_set_item = "tf.TensorListSetItem"(%tl, %idx, %elem_1) : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<i32>, tensor<8xf32>) -> tensor<!tf_type.variant<tensor<*xf32>>>
    %elem_shape_2 = "tf.Const"() {value = dense<-1> : tensor<1xi32>} : () -> tensor<1xi32>

  %2 = "tf.BatchToSpaceND"(%1, %cst, %cst_1) : (tensor<4x64x64x?xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<1x128x128x?xf32>
  func.return %2 : tensor<1x128x128x?xf32>

  // CHECK-LABEL: testNoDilatedConvWhenLastDimIsDynamic
  // CHECK: [[STB:%.*]] = "tf.SpaceToBatchND"
  // CHECK-NEXT: [[CONV:%.*]] = "tf.Conv2D"
  // CHECK-NEXT: [[RESULT:%.*]] = "tf.BatchToSpaceND"

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

func.func private @add(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32>  {
  %0 = "tf.Add"(%arg0, %arg1): (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

func.func private @mul(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32>  {
  %0 = "tf.Multiply"(%arg0, %arg1): (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

  func.return %0 : tensor<4xf16>
}

func.func @f32() -> tensor<4xf32> {
  // CHECK-LABEL: @f32
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf32>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32> } : () -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64

  func.return %0 : tensor<4xf16>
}

func.func @f32() -> tensor<4xf32> {
  // CHECK-LABEL: @f32
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf32>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32> } : () -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64

// expected-error@-1 {{'tf_executor.Merge' op expects all operands to be broadcastable with output type but got 'tensor<4xf32>' vs 'tensor<8xf32>'}}
    tf_executor.fetch %value : tensor<8xf32>
  }
  func.return %result : tensor<8xf32>
}

// -----

// Check that merge data inputs of variant type are broadcastable to the output

  %3 = "tf.AddN"(%1, %arg0, %1) : (tensor<2xf32>, tensor<2xf32> , tensor<2xf32>) -> tensor<2xf32>
  %4 = "tf.AddN"(%1, %1) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  %5 = "tf.AddN"(%arg0, %1, %0) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  func.return %2, %3, %4, %5: tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>
}

// CHECK-LABEL: func @addNWithZerosInt

    "tfl.yield"(%1#0, %1#1) : (tensor<*xi32>, tensor<*xf32>) -> ()
  }) : (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>) loc("WhileOp")
  // CHECK: (tensor<i32>, tensor<1xf32>, tensor<i32>) ->
  // CHECK-SAME: (tensor<i32>, tensor<1xf32>, tensor<i32>)
  func.return %0#1 : tensor<1xf32>
}

func.func private @WhileOp_cond(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>, %arg2: tensor<i32>) -> tensor<i1> {

    // CHECK: %[[MUL:.*]] = "tf.Mul"{{.*}} (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    // CHECK: %[[UNKNOWN:.*]] = "tf.Unknown"(%[[MUL]], %[[MUL]]) : (tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
    // CHECK: return %[[UNKNOWN]] : tensor<*xf32>
    %0 = "tf.Mul"(%arg0, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
    %1 = "tf.Unknown"(%0, %0) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

  // return %[[SUM2]]
  %0 = "tf.AddN"(%arg0, %arg1, %arg2, %arg3) : (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

// CHECK-LABEL: func @addN_5
func.func @addN_5(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>, %arg2: tensor<*xf32>, %arg3: tensor<*xf32>, %arg4: tensor<*xf32>) -> tensor<*xf32> {
  // CHECK: %[[SUM0:.*]] = "tf.AddV2"(%arg0, %arg1)