"builtin.module"() ({
  "func.func"() ({
  ^bb0(%arg0: tensor<i32>):
    %1617 = "tf.While"(%arg0) {_lower_using_switch_merge = true, _num_original_outputs = 7 : i64, _read_only_resource_inputs = [], body = @main_while_body_4225150, cond = @main_while_cond_4225140, device = "", is_stateless = false, parallel_iterations = 10 : i64, shape_invariant} : (tensor<i32>) -> (tensor<i32>)
    "func.return"() : () -> ()

  // CHECK: RemoteCall
  // CHECK-SAME: (tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<i64>) -> tensor<!tf_type.string>
  %0 = "tf.RemoteCall"(%arg1, %arg0, %arg2) <{f = @__inference__next_func_3760}> {device = ""} : (tensor<!tf_type.string>, tensor<!tf_type.string>, tensor<i64>) -> tensor<!tf_type.string>
  return
}

    %0 = "tf.Const"() {value = dense<1> : tensor<i64>} : () -> tensor<i64>
    %1 = "tf.Const"() {value = dense<0> : tensor<1x1xi32>} : () -> tensor<1x1xi32>

    %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = [[0, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x4x2xf32>
    return %0 : tensor<1x3x4x2xf32>
  }
}

// CHECK-LABEL: quantize_conv_per_tensor
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x3x4x3xf32>

  }

  // CHECK-LABEL: multiple_return_values
  func.func @multiple_return_values(%arg0: tensor<3xi64>) -> tensor<i64> {
     %0:3 = "tf.Unpack"(%arg0) {axis = 0 : i64} : (tensor<3xi64>) -> (tensor<i64>, tensor<i64>, tensor<i64>)
    // CHECK: return %1 : tensor<i64>
    func.return %0#0 : tensor<i64>
  }

  // CHECK-LABEL: constant_parameter
  func.func @constant_parameter(%arg0: tensor<2xf32>) -> tensor<2xf32> {

  /// TaggedValue constructor for type INT64.
  explicit TaggedValue(int64_t i64) : type_(INT64), data_(Int64(i64)) {}
  /// TaggedValue constructor for type FLOAT32.
  explicit TaggedValue(Float32 f32) : type_(FLOAT32), data_(f32) {}
  /// TaggedValue constructor for type INT64.
  explicit TaggedValue(Int64 i64) : type_(INT64), data_(i64) {}
  /// TaggedValue constructor for type STRING.

  func.return %0 : tensor<1x1x5x6x7xf32>
  // CHECK-LABEL: strided_slice_big_dims

  // CHECK: [[max_iter:%.*]] = "tf.StridedSlice"
  %outputs_14  =  "tf.StridedSlice"(%outputs_12, %outputs_2, %outputs_4, %outputs_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<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>
  // CHECK: [[tensor_list:%.*]] = "tf.TensorListReserve"

  // CHECK-DAG: %[[RESULT:.+]] = mhlo.add %[[X_NORMED]], %[[RHS_BCAST]] : tensor<4x256xf32>
  %0 = "mhlo.batch_norm_inference"(%x, %scale, %offset, %mean, %variance)
      {epsilon = 1.001000e-05 : f32, feature_index = 1 : i64} :
      (tensor<4x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>,
        tensor<256xf32>) -> tensor<4x256xf32>
  // CHECK-DAG: return %[[RESULT]]
  func.return %0 : tensor<4x256xf32>
}

// CHECK:           %[[VAL_1:.*]] = "tf.Const"() <{value = dense<1> : tensor<i64>}> : () -> tensor<i64>
// CHECK:           return %[[VAL_1]] : tensor<i64>
// CHECK:         }
func.func @size_rank_one_i64(%arg0: tensor<f32>) -> tensor<i64> {
  %0 = mhlo.constant dense<1> : tensor<i64>
  func.return %0 : tensor<i64>
}

// CHECK-LABEL:   func @complex(