}) {n = 2 : i32, operandSegmentSizes = array<i32: 0, 0>} : () -> ()
  func.return
}

// -----

// Check that an empty replicate block is invalid.
func.func @verifier_replicate_empty_block() {
  "tf_device.replicate" () ({
// expected-error@-1 {{'tf_device.replicate' op expects a non-empty block}}
  ^entry:
  }) {n = 2 : i32, operandSegmentSizes = array<i32: 0, 0>} : () -> ()
  func.return
}

// CHECK: func @main([[MAIN_ARG0:%.*]]: tensor<i32>) -> tensor<i32>
func.func @main(%arg0: tensor<i32>) -> tensor<i32> {
  // CHECK:      [[MAIN_TOKEN:%.*]] = mhlo.create_token

  // CHECK:      [[CALL:%.*]]:2 = call @callee([[MAIN_ARG0]], [[MAIN_TOKEN]])
  // CHECK-SAME: (tensor<i32>, !mhlo.token) -> (tensor<i32>, !mhlo.token)
  %0 = func.call @callee(%arg0) : (tensor<i32>) -> tensor<i32>

  // CHECK:      return [[CALL]]#0

    // expected-remark@+1 {{Cost: 1}}
    %12 = "tf.StridedSlice"(%8, %6, %1, %4) {begin_mask = 0 : i64, device = "/job:localhost/replica:0/task:0/device:CPU:0", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<4xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>
    // expected-remark@+1 {{Cost: 5}}

  // CHECK-NEXT: "tf.Const"() <{value = dense<> : tensor<0xi32>}>
  %elem_shape = "tf.Const"() {value = dense<> : tensor<0xi32>} : () -> tensor<0xi32>
  // CHECK-NEXT: %[[NUM:.*]] = "tf.Const"() <{value = dense<10> : tensor<i32>}>
  %num = "tf.Const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>
  // CHECK-NEXT: %[[ZERO_SCALAR:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>

        tf_device.return %1 : tensor<i32>
      }) : () -> tensor<i32>

      return %0 : tensor<i32>
    }

    func.func @pcall_func_with_resources_0(%arg0: tensor<i32>, %arg1: tensor<!tf_type.resource>) -> tensor<i32> {
      return %arg0 : tensor<i32>
    }
    ```

  %0 = tf_executor.graph {
    %outputs, %control = tf_executor.island {
      %1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
      "tf.AssignVariableOp"(%arg0, %1) : (tensor<*x!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
      tf_executor.yield %1 : tensor<i32>
    }
    tf_executor.fetch %outputs : tensor<i32>
  }
  func.return %0 : tensor<i32>

    "tfl.yield"(%13, %arg2, %11, %arg4, %12) : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<!tf_type.resource>, tensor<f32>) -> ()
  }) {is_stateless = false} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<!tf_type.resource>, tensor<f32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<!tf_type.resource>, tensor<f32>)
  func.return %5#3 : tensor<!tf_type.resource>

// CHECK: "tfl.while"

  %conv = "tfl.conv_2d"(%0, %w, %b) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x224x224x3xf32>, tensor<64x3x3x3xf32>, tensor<64xf32>) -> tensor<1x112x112x64xf32>

          tf_device.return %4 : tensor<i64>
        }) : () -> (tensor<i32>, tensor<i64>)
        tf_device.return %3#0, %3#1 : tensor<i32>, tensor<i64>
      }
      tf_executor.yield %2#0, %2#1, %2#2, %2#3 : tensor<i32>, tensor<i32>, tensor<i64>, tensor<i64>
    }
    tf_executor.fetch %1#0, %1#1, %1#2, %1#3 : tensor<i32>, tensor<i32>, tensor<i64>, tensor<i64>
  }
  func.return
}

  %2 = "tfl.pseudo_qconst"() {qtype = tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>, value = dense<0> : tensor<32xi32>} : () -> tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>