%split_dim = arith.constant dense<0> : tensor<i32>
  // expected-error @+1 {{'tfl.split' op output #0 should be 'tensor<8x4xf32>'}}
  %0, %1 = "tfl.split"(%split_dim, %arg0) {num_splits = 2 : i32} : (tensor<i32>, tensor<16x4xf32>) -> (tensor<8x2xf32>, tensor<8x2xf32>)
  func.return %0, %1 : tensor<8x2xf32>, tensor<8x2xf32>
}

// -----

  %0 = "tf.Squeeze"(%arg0) : (tensor<1x2x2xf32>) -> tensor<2x2xf32>
  func.return %0 : tensor<2x2xf32>

// CHECK-LABEL:squeezeDefault
// CHECK:  "tfl.squeeze"(%arg0) <{squeeze_dims = []}> : (tensor<1x2x2xf32>) -> tensor<2x2xf32>
}

func.func @squeezeSingleAxis(%arg0: tensor<2x1x2xf32>) -> tensor<2x2xf32> {
  %0 = "tf.Squeeze"(%arg0) {squeeze_dims = [1]} : (tensor<2x1x2xf32>) -> tensor<2x2xf32>

func.func @testReshapeNoOp(%arg0: tensor<2x4xf32>, %arg1: tensor<2xi32>) -> tensor<2x4xf32> {
  %0 = "tf.Reshape"(%arg0, %arg1) : (tensor<2x4xf32>, tensor<2xi32>) -> tensor<2x4xf32>

  // CHECK: return %arg0
  func.return %0 : tensor<2x4xf32>
}

// CHECK-LABEL: func @testBroadcastToNoOp
func.func @testBroadcastToNoOp(%arg0: tensor<2x4xf32>, %arg1: tensor<2xi32>) -> tensor<2x4xf32> {

  %0 = stablehlo.uniform_dequantize %arg : (tensor<2x2x!quant.uniform<i8:f32, 1.000000e+0:8>>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}
// CHECK-LABEL: uniform_dequantize_op
// CHECK: %[[DEQUANT:.+]] = "tfl.dequantize"({{.*}}) : (tensor<2x2x!quant.uniform<i8:f32, 1.000000e+00:8>>) -> tensor<2x2xf32>
// CHECK: return %[[DEQUANT]]

// -----

        %6 = "tf.TensorListSetItem"(%3, %4, %5) {device = ""} : (tensor<!tf_type.variant<tensor<*xf32>>>, tensor<i32>, tensor<2x2xf32>)-> tensor<*x!tf_type.variant>
        %7 = "tf.Const"() {device = "", value = dense<-1> : tensor<i32>} : () -> tensor<i32>

  return
}
```

will be transformed to:

```mlir
func @main(%arg0: tensor<8x4xf32>) {
  // EmptyTensorList lowering
  %emptyi = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %emptyf = "tf.Cast"(%emptyi) : (tensor<i32>) -> tensor<f32>
  %size_shape = "tf.Const"() {value = dense<[10, 8, 4]> : tensor<3xi32>} : () -> tensor<3xi32>

// dimensions into a single dimension. For example,
//
//   %shape = arith.constant dense<[1, 128, 64]> : tensor<3xi32>
//   %reshape = tfl.reshape(%input, %shape) // %input: tensor<128x64xf32>
//   %fc = tfl.fully_connected(%reshape, %filter, %bias)
//           {keep_num_dims = false, weights_format = "DEFAULT"}
//
// can be canonicalized to
//