%arg1 = arith.constant dense<0.0> : tensor<f32>
  %arg2 = arith.constant dense<15.0> : tensor<f32>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %0 : tensor<8xf32>

// CHECK:  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %cst, %cst_0)

  %arg1 = arith.constant dense<0.0> : tensor<f32>
  %arg2 = arith.constant dense<255.0> : tensor<f32>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 5, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %0 : tensor<8xf32>

// CHECK:  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %cst, %cst_0)

//   \  (tf.Identity) (tf.Identity)   =>    \  (tf.Identity) (tf.Identity)
//    \     |             |                  \     |             |
//       tf.FakeQuantWithMinMaxVars       tf.FakeQuantWithMinMaxVars
//                   |                                 |
//                                                tf.quantize
//                                                     |

//   \  (tf.Identity) (tf.Identity)   =>    \  (tf.Identity) (tf.Identity)
//    \     |             |                  \     |             |
//       tf.FakeQuantWithMinMaxVars       tf.FakeQuantWithMinMaxVars
//                   |                                 |
//                                                tfl.quantize
//                                                     |

// input   min cst       max cst              input
//  \       |             |                     |
//   \  (tf.Identity) (tf.Identity)   =>   quant.qcast
//    \     |             |                     |
//       tf.FakeQuantWithMinMaxVars        quant.dcast
//                   |                          |
//
// Warns if the (most likely unwanted, currently not quite correctly handled)
// case of back-to-back tf.FakeQuant occurs
//