"tf.AssignVariableOp"(%arg18, %9#0) : (tensor<*x!tf_type.resource<tensor<f32>>>, tensor<f32>) -> ()
      "tf.AssignVariableOp"(%arg19, %9#1) : (tensor<*x!tf_type.resource<tensor<f32>>>, tensor<f32>) -> ()

}

func.func @batchmatmulv2_adj_complex(%arg0: tensor<2x5xcomplex<f32>>, %arg1: tensor<4x2xcomplex<f32>>) -> tensor<5x4xcomplex<f32>> {
// CHECK-LABEL:   func @batchmatmulv2_adj_complex(
// CHECK-SAME:                                    [[LHS:%.*]]: tensor<2x5xcomplex<f32>>, [[RHS:%.*]]: tensor<4x2xcomplex<f32>>) -> tensor<5x4xcomplex<f32>> {
// CHECK:           [[LHSRE:%.*]] = mhlo.real [[LHS]]

  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<255.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %minc = "tf.Cast"(%mini) : (tensor<f32>) -> tensor<f32>
  %maxc = "tf.Cast"(%maxi) : (tensor<f32>) -> tensor<f32>

// CHECK: return %[[UNIFORM_QUANTIZE_1]] : tensor<1x3x!quant.uniform<i8:f32, {{.*}}>>

// CHECK-PER-TENSOR: func.func private @quantized_dot_general_fn(%[[ARG_1:.+]]: tensor<1x2x!quant.uniform<i8:f32, {{.*}}>>, %[[ARG_2:.+]]: tensor<2x3x!quant.uniform<i8<-127:127>:f32, {{.*}}>>) -> tensor<1x3x!quant.uniform<i8:f32, {{.*}}>> attributes {_from_xla_call_module}

// CHECK-SAME:  [[VAR:%.*]]: tensor<f32>, [[MG:%.*]]: tensor<f32>, [[MS:%.*]]: tensor<f32>, [[MOM:%.*]]: tensor<f32>, [[LR:%.*]]: tensor<f32>, [[RHO:%.*]]: tensor<f32>, [[MOMENTUM:%.*]]: tensor<f32>, [[EPSILON:%.*]]: tensor<f32>, [[GRAD:%.*]]: tensor<f32>

func.func @allowsMHLO() -> (tensor<8x64x32x4xcomplex<f32>> {mhlo.sharding = ""}) {
  %0 = mhlo.constant dense<(1.000000e+00,-1.000000e+00)> : tensor<128x32x4xcomplex<f32>>
  %1 = mhlo.constant dense<(1.000000e+00,1.000000e+00)> : tensor<8x64x128xcomplex<f32>>
  %2 = "mhlo.einsum"(%1, %0) <{einsum_config = "abc,cde->abde"}> : (tensor<8x64x128xcomplex<f32>>, tensor<128x32x4xcomplex<f32>>) -> tensor<8x64x32x4xcomplex<f32>>
  return %2 : tensor<8x64x32x4xcomplex<f32>>
}

    %0 = "tf.Const"() {value = dense<4.200000e+01> : tensor<f32>} : () -> tensor<f32>
    %1 = "tf.VarHandleOp"() {container = "", shared_name = "x"} : () -> tensor<!tf_type.resource<tensor<f32>>>
    %2 = "tf.ReadVariableOp"(%1) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
    %3 = "tf.AddV2"(%2, %0) : (tensor<f32>, tensor<f32>) -> tensor<f32>
    %4 = "tf.Pack"(%0, %3) : (tensor<f32>, tensor<f32>) -> tensor<2xf32>
    func.return %4 : tensor<2xf32>

    %8 = "mhlo.compare"(%arg1, %arg1) {comparison_direction = #mhlo<comparison_direction NE>} : (tensor<f32>, tensor<f32>) -> tensor<i1>
    %9 = mhlo.or %7, %8 : tensor<i1>
    %10 = "mhlo.select"(%9, %arg1, %arg3) : (tensor<i1>, tensor<f32>, tensor<f32>) -> tensor<f32>
    %11 = "mhlo.compare"(%arg1, %arg3) {comparison_direction = #mhlo<comparison_direction EQ>} : (tensor<f32>, tensor<f32>) -> tensor<i1>

  tf_executor.graph {
    // CHECK: tf_executor.island
    // CHECK-NOT: wraps
    %0:3 = tf_executor.island {
      %1:2 = "tf.IdentityN"(%arg0, %arg1) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>) loc("identity@some_function")
      %2:2 = "tf.IdentityN"(%1#0, %1#1) : (tensor<i32>, tensor<f32>) -> (tensor<i32>, tensor<f32>) loc("identity@some_function")

  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<15.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %minc = "tf.Cast"(%mini) : (tensor<f32>) -> tensor<f32>
  %maxc = "tf.Cast"(%maxi) : (tensor<f32>) -> tensor<f32>