%0 = arith.constant dense<-1> : tensor<1xi32>
  %1 = "tfl.reduce_max"(%arg0, %0) {keep_dims = true} : (tensor<10x10xf32>, tensor<1xi32>) -> tensor<10x1xf32>
  %2 = tfl.sub(%arg0, %1) {fused_activation_function = "NONE"} : (tensor<10x10xf32>, tensor<10x1xf32>) -> tensor<10x10xf32>
  %3 = "tfl.exp"(%2) : (tensor<10x10xf32>) -> tensor<10x10xf32>

  func.return %0 : tensor<2x2xf16>
}

// -----

// CHECK-LABEL: squeeze
func.func @squeeze(%arg0: tensor<1x1x10xf32>) -> tensor<1x10xf32> {
  // CHECK: mhlo.reshape
  %0 = "tf.Squeeze"(%arg0) : (tensor<1x1x10xf32>) -> tensor<1x10xf32>
  func.return %0 : tensor<1x10xf32>
}

// -----

// CHECK-LABEL: squeeze_ranked
func.func @squeeze_ranked(%arg0: tensor<?x?x?xf32>) -> tensor<?xf32> {

  %0 = "mhlo.dot"(%arg0, %arg1) {precision_config = [#mhlo<precision DEFAULT>, #mhlo<precision DEFAULT>]} : (tensor<1x256xf32>, tensor<256x1xf32>) -> tensor<1x1xf32>
  func.return %0 : tensor<1x1xf32>
}

// CHECK-LABEL:   func @broadcast_in_dim_tf_style(
// CHECK-SAME:                                    %[[VAL_0:.*]]: tensor<8x1x16xf32>) -> tensor<3x8x8x16xf32> {