%7 = "tfl.reduce_all"(%arg, %axis_2) { keep_dims = true } : (tensor<1x2x1x3xi1>, tensor<1xi32>) -> tensor<1x2x1x3xi1>
  %8 = "tfl.reduce_all"(%arg, %axis_3) { keep_dims = false } : (tensor<1x2x1x3xi1>, tensor<1xi32>) -> tensor<1x2x1xi1>
  %9 = "tfl.reduce_all"(%arg, %axis_3) { keep_dims = true } : (tensor<1x2x1x3xi1>, tensor<1xi32>) -> tensor<1x2x1x1xi1>

// CHECK-SAME:                      %[[VAL_0:.*]]: tensor<5x3x1xi1>) -> tensor<5x3x1xi1> {
// CHECK:           %[[VAL_1:.*]] = "tf.LogicalNot"(%[[VAL_0]]) : {{.*}} -> tensor<5x3x1xi1>
// CHECK:           return %[[VAL_1]] : tensor<5x3x1xi1>
// CHECK:         }
func.func @convert_not(%arg0: tensor<5x3x1xi1>) -> tensor<5x3x1xi1> {
  %0 = "mhlo.not"(%arg0): (tensor<5x3x1xi1>) -> (tensor<5x3x1xi1>)
  func.return %0 : tensor<5x3x1xi1>
}

// CHECK-NEXT:  return %[[q]] : tensor<1x2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
}

// CHECK-LABEL: QuantizeAssignVariableWithDequantAndEqualType
func.func @QuantizeAssignVariableWithDequantAndEqualType(%arg0 : tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>) -> tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>> {

    %8 = stablehlo.constant dense<-5> : tensor<1x1x1xi8>  // Output zero point (z3).
    %9 = stablehlo.constant dense<1.250000e+01> : tensor<1x1x1xf32>  // Merged scale (s1 * s2).
    %10 = call @uniform_quantize(%arg0, %1, %2) : (tensor<8x16x16xf32>, tensor<1x1x1xf32>, tensor<1x1x1xi8>) -> tensor<8x16x16xi8>  // q1
    %11 = call @uniform_quantize_0(%arg1, %4, %5) : (tensor<8x16x4xf32>, tensor<1x1x1xf32>, tensor<1x1x1xi8>) -> tensor<8x16x4xi8>  // q2

func.func @equal_broadcast(%arg0: tensor<1xi32>, %arg1: tensor<1x2xi32>) -> tensor<1x2xi1> {
  // CHECK-DAG: %[[LHS_BCAST:.+]] = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<1> : tensor<1xi64>}>
  // CHECK-NEXT: mhlo.compare EQ, %[[LHS_BCAST]], %arg1
  %0 = "tf.Equal"(%arg0, %arg1) : (tensor<1xi32>, tensor<1x2xi32>) -> tensor<1x2xi1>
  func.return %0: tensor<1x2xi1>
}

// CHECK-LABEL: func @equal_broadcast_no_incompatible_shapes_error

  // CHECK: return %[[ADD]] : tensor<14xf32>
}

func.func @scatter_nd_updates_bool(%arg0: tensor<1x24xi1>, %arg1: tensor<1x2x2xi32>, %arg2: tensor<1x2xi1>) -> tensor<1x24xi1> {
  %0 = "tf.TensorScatterUpdate"(%arg0, %arg1, %arg2) : (tensor<1x24xi1>, tensor<1x2x2xi32>, tensor<1x2xi1>) -> tensor<1x24xi1>
  func.return %0 : tensor<1x24xi1>

// CHECK-LABEL: scatter_nd_updates_bool(

//CHECK-NEXT:}

func.func @select(%arg0: tensor<1x30x1xi1>, %arg1: tensor<1x30x1xi32>, %arg2: tensor<1x30x1xi32>) -> tensor<1x30x1xi32> {
 %0 = "vhlo.select_v1" (%arg0, %arg1, %arg2) : (tensor<1x30x1xi1>, tensor<1x30x1xi32>, tensor<1x30x1xi32>) -> tensor<1x30x1xi32>
 func.return %0 : tensor<1x30x1xi32>
}

  %0 = "tf.SelectV2"(%arg2, %arg0, %arg1) : (tensor<8x7x6x5x?x3x2x1xi1>, tensor<8x7x6x5x?x3x2x1xf32>, tensor<*xf32>) -> tensor<8x7x6x5x?x3x2x1xf32>
  func.return %0 : tensor<8x7x6x5x?x3x2x1xf32>

  // CHECK-LABEL:select_v2_with_unranked_rhs

  %3 = "tfl.shape"(%arg0) : (tensor<8x7x6x5x?x3x2x1xi1>) -> tensor<8xi64>
  %4 = "tfl.broadcast_args"(%2, %3) : (tensor<8xi64>, tensor<8xi64>) -> tensor<8xi64>
  %5 = "tfl.broadcast_to"(%arg0, %4) : (tensor<8x7x6x5x?x3x2x1xi1>, tensor<8xi64>) -> tensor<8x7x6x5x?x3x2x1xi1>
  %6 = "tfl.broadcast_to"(%arg1, %4) : (tensor<8x7x6x5x?x3x2x1xf32>, tensor<8xi64>) -> tensor<8x7x6x5x?x3x2x1xf32>

// CHECK: %[[EXPAND1:.+]] = "tfl.expand_dims"(%[[EXPAND0]], %[[EXPAND_DIM1]]) : (tensor<1x2x1x!quant.uniform<i8:f32, 2.000000e+00:3>>, tensor<1xi32>) -> tensor<1x2x1x1x!quant.uniform<i8:f32, 2.000000e+00:3>>