%control_11 = tf_executor.island wraps "tf.LoadTPUEmbeddingAdagradParameters"(%outputs_2, %outputs_5) {config = "", num_shards = 1 : i64, shard_id = 0 : i64, table_id = -1 : i64, table_name = "table4"} : (tensor<8xf32>, tensor<8xf32>) -> ()
    tf_executor.fetch
  }
  return
}

  %filter = mhlo.constant dense<[[[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]]> : tensor<1x1x3x2xf32>

  // CHECK-NOT: corert_tensorhandle_to_fallback_tensor

  %0 = tfrt_fallback_async.fallback_tensor_to_corert_tensorhandle %arg {_tfrt_cost = 1 : i64, device = "/CPU:0"} : (!tfrt_fallback.tf_tensor) -> (!corert.tensorhandle)
  %1 = tfrt_fallback_async.corert_tensorhandle_to_fallback_tensor %0 {_tfrt_cost = 1 : i64, device = "/CPU:0"} : (!corert.tensorhandle) -> (!tfrt_fallback.tf_tensor)

  // CHECK: tfrt.return [[arg]]

func.func @serving_default(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  %0 = "tf.BatchFunction"(%arg0, %arg1) {f = @batched_func, num_batch_threads = 1 : i64, max_batch_size = 2 : i64, batch_timeout_micros = 10000 : i64, operandSegmentSizes = array<i32: 1, 1>} : (tensor<1xf32>, tensor<1xf32>) -> (tensor<1xf32>)
  return %0 : tensor<1xf32>
}
// The contents of `@serving_default` should have been inlined to `@batch_func`.

    // expected-error @+1 {{'tf.TPUPartitionedInput' op TF2XLA TPU bridge input check: number of inputs inconsistent. num_cores_per_replica=2 no. of inputs=3}}
    %pi, %c0 = tf_executor.island wraps "tf.TPUPartitionedInput"(%arg0, %arg1, %arg1) {index = 1 : i64} : (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<i32>

    %3:2 = tf_executor.island wraps "tf.While"(%1#0) {name = "B", body = @while_body_with_wrong_cluster_attr, cond = @while_cond_with_wrong_cluster_attr, is_stateless = false, parallel_iterations = 10 : i64} : (tensor<i32>) -> tensor<i32>

    %0 = "tf.Equal"(%arg0, %cst) {_tpu_replicate = "cluster", device = "", incompatible_shape_error = true} : (tensor<*xi64>, tensor<i64>) -> tensor<*xi1>

// CHECK-LABEL: func private @quantized_matmul_fn_0
// CHECK:  %0 = "tf.UniformQuantizedDotHybrid"(%arg0, %arg1, %arg2, %arg3)
// CHECK-SAME: rhs_quantization_axis = -1 : i64
// CHECK-SAME: rhs_quantization_max_val = 127 : i64
// CHECK-SAME: rhs_quantization_min_val = -128 : i64

}

// -----

module {
  func.func @conv(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>, tensor<*xf32>) {

      lhs_quantization_axis = -1 : i64,
      lhs_quantization_min_val = -128 : i64,
      lhs_quantization_max_val = 127 : i64,
      rhs_quantization_axis = -1 : i64,
      rhs_quantization_min_val = -128 : i64,
      rhs_quantization_max_val = 127 : i64,
      output_quantization_axis = -1 : i64,
      output_quantization_min_val = -2147483648 : i64,
      output_quantization_max_val = 2147483647 : i64} : (

  tf_executor.graph {