func.func @all_keep_dim(%input: tensor<4x8xi1>) -> tensor<4x1xi1> {
  // CHECK: mhlo.reshape %{{.*}} : (tensor<4xi1>) -> tensor<4x1xi1>
  %dims = "tf.Const"() { value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %0 = "tf.All"(%input, %dims) {keep_dims = true} : (tensor<4x8xi1>, tensor<1xi32>) -> tensor<4x1xi1>
  func.return %0 : tensor<4x1xi1>
}

// -----

// CHECK-LABEL: @all_dynamic

}

func.func @concat_v2_with_bool_type(%arg0: tensor<?x1xi1>, %arg1: tensor<?x1xi1>) -> tensor<?x2xi1> {
  %0 = "tf.Const"() { value = dense<-1> : tensor<i32> } : () -> tensor<i32>
  %1 = "tf.ConcatV2"(%arg0, %arg1, %0) : (tensor<?x1xi1>, tensor<?x1xi1>, tensor<i32>) -> tensor<?x2xi1>
  func.return %1 : tensor<?x2xi1>

// CHECK-LABEL: concat_v2_with_bool_type

  %20 = "tf.ExpandDims"(%19, %7) {device = ""} : (tensor<1xi32>, tensor<i32>) -> tensor<1x1xi32>
  %21 = "tf.Cast"(%20) {Truncate = false, device = ""} : (tensor<1x1xi32>) -> tensor<1x1xi64>
  %22 = "tf.Reshape"(%21, %12) {device = ""} : (tensor<1x1xi64>, tensor<1xi64>) -> tensor<1xi64>
  %23 = "tf.Reshape"(%arg0, %5) {device = ""} : (tensor<1x!tf_type.string>, tensor<1xi32>) -> tensor<1x!tf_type.string>

// CHECK-NEXT: %[[q0:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<1x1x!quant.uniform<u8:f32, 0.0024715415402954701:178>>}> {volatile} : (tensor<1x1xf32>) -> tensor<1x1x!quant.uniform<u8:f32, 0.0024715415402954701:178>>
// CHECK-NEXT: %[[r0q0:.*]] = "tfl.quantize"(%[[q0]]) <{qtype = tensor<1x1x!quant.uniform<u8:f32, 0.0026575490540149166:184>>}> : (tensor<1x1x!quant.uniform<u8:f32, 0.0024715415402954701:178>>) -> tensor<1x1x!quant.uniform<u8:f32, 0.0026575490540149166:184>>