module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1504 : i32}} {
    func.func @main() -> tensor<?xi32> attributes {tf.entry_function = {control_outputs = "", inputs = "i,j", outputs = "identity_RetVal"}} {
      %0 = mhlo.constant dense<1.000000e+00> : tensor<f64>
      %1 = mhlo.convert %0 : (tensor<f64>) -> tensor<i64>
      %2 = mhlo.reshape %1 : (tensor<i64>) -> tensor<1xi64>

  return %cst_0 : tensor<8x64x128xcomplex<f32>>
}

// -----

func.func @nonstatic_shape_mhlo() -> tensor<?xi32> attributes {tf.entry_function = {control_outputs = "", inputs = "i,j", outputs = "identity_RetVal"}} {
  %0 = mhlo.constant dense<1.000000e+00> : tensor<f64>
  %1 = mhlo.convert %0 : (tensor<f64>) -> tensor<i64>
  %2 = mhlo.reshape %1 : (tensor<i64>) -> tensor<1xi64>

    %0:6 = tf_executor.graph {

    %0:3 = "tf.Unpack"(%arg0) {axis = 0 : i64} : (tensor<3xi64>) -> (tensor<i64>, tensor<i64>, tensor<i64>)
    return
  }
})";

    %0:3 = "tf.Unpack"(%arg0) {axis = 0 : i64} : (tensor<3xi64>) -> (tensor<i64>, tensor<i64>, tensor<i64>)
    return
  }
})";

XlaComputation GetTestXlaComputation() {

// CHECK-LABEL: while_with_id_passthrough
func.func @while_with_id_passthrough(%arg0: tensor<7xf32> {tf._user_specified_name = "x"}) -> tensor<?xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "x", outputs = "identity_RetVal"}} {
  %0 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %1 = "tf.Const"() {value = dense<3> : tensor<i32>} : () -> tensor<i32>

  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "identity_RetVal"
  op: "_Retval"
  input: "training_1/Identity"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }