// CHECK: return %[[CALL]]

// CHECK: quantized_dot_general_fn
// CHECK-SAME: (%[[ARG1:.+]]: tensor<1x2xf32>,  %[[ARG2:.+]]: tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>) -> tensor<1x3xf32>
// CHECK: %[[DOT:.+]] = stablehlo.dot_general %[[ARG1]], %[[ARG2]]
// CHECK-SAME: (tensor<1x2xf32>, tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>) -> tensor<1x3xf32>

// CHECK: func.func @init_func_restore_op
// CHECK-SAME: tf_saved_model.exported_names = ["tf_saved_model.session_initializer_restore_op"]
// CHECK-SAME: tf_saved_model.initializer_type = "restore_op"

// Test that a new save function that wraps the SaveV2 op is created.
// CHECK: func.func private @tf_quant__save(%[[ARG:.*]]: tensor<!tf_type.string>)
// CHECK: %[[VAR_HANDLE_0:.*]] = "tf.VarHandleOp"()
// CHECK-SAME: {{.*shared_name = "var_0".*}}

    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
library {
}
versions {
}

# CHECK-LABEL: func @main
# CHECK-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<f32>) -> tensor<*xf32>
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "output_node"
# CHECK:         %[[GRAPH:[0-9]+]] = tf_executor.graph

func.func @does_reordering_for_nested_compiles() -> () {
   // CHECK: _TPUCompileMlir
   // CHECK-SAME: Z
   // CHECK: tf_device.launch
   // CHECK-NEXT: _TPUCompileMlir
   // CHECK-SAME: X
   // CHECK: tf_device.launch
   // CHECK-NEXT: _TPUCompileMlir
   // CHECK-SAME: Y
   // CHECK: OpA
   // CHECK: OpB
   // CHECK: OpC
   "tf.OpA"() : () -> ()
   "tf_device.launch"() ({

// CHECK-LABEL @export_three
module @export_three attributes {tf_saved_model.semantics} {
  // CHECK: func @a
  // CHECK-SAME: i32
  // CHECK-SAME: f32
  // CHECK-SAME: tensor<i32>
  // CHECK: [[tuple:%.*]]:2 = call @f
  // CHECK: return [[tuple]]#0, [[tuple]]#1
  // CHECK-SAME: tensor<i8>
  // CHECK-SAME: tensor<i16>
  // CHECK: func @b
  // CHECK: func @c
  // CHECK: func private @f
  // CHECK-NOT: exported_names

  // CHECK: "tf.XlaHostCompute"(%arg0, %arg1)
  // CHECK-SAME: ancestors = []
  // CHECK-SAME: cost_estimate_ns = 1000000 : i64
  // CHECK-SAME: key = ""
  // CHECK-SAME: recv_key = "host_compute_channel_recv"
  // CHECK-SAME: send_key = "host_compute_channel_send"
  // CHECK-SAME: shape_inference_graph = @host_func
  // CHECK-SAME: shapes = [#tf_type.shape<*>, #tf_type.shape<3x?>]
  // CHECK-SAME: tpu_core = 0 : i64
  // CHECK: func @host_func

  // CHECK-SAME: ([[serialized]]
  // CHECK-NOT: device
  // CHECK-SAME: Tdense = [f32, f32]
  // CHECK-SAME: dense_shapes = [#corert.shape<>, #corert.shape<>]
  // CHECK-SAME: num_sparse = 2 : i64
  // CHECK-SAME: ragged_split_types = []
  // CHECK-SAME: ragged_value_types = []
  // CHECK-SAME: sparse_types = [!corert.string, i64]

  func.return %0 : tensor<i32>
}

// CHECK-LABEL: func private @merge_stateless_merged_if_0_0_then
// CHECK-SAME: ([[x:%.*]]: tensor<i32>, [[y:%.*]]: tensor<i32>)
// CHECK: [[r0:%.*]] = "tf.AddV2"([[x]], [[y]])
// CHECK: return [[r0]], [[r0]], [[r0]]

// CHECK-LABEL: func private @merge_stateless_merged_if_0_0_else
// CHECK-SAME: ([[x:%.*]]: tensor<i32>, [[y:%.*]]: tensor<i32>)

  // CHECK: "tf_saved_model.session_initializer"()
  // CHECK-SAME: initializers = [@init_func_restore_op]

  func.func @init_func_restore_op() -> () attributes {
      tf_saved_model.initializer_type = "restore_op",
      tf_saved_model.exported_names = ["tf_saved_model.session_initializer_restore_op"]} {
    return
  }
  // CHECK: @init_func_restore_op
  // CHECK-SAME: tf_saved_model.initializer_type = "restore_op"

  // CHECK:      "tf.EnqueueTPUEmbeddingArbitraryTensorBatch"
  // CHECK-SAME:   device_ordinal = 0
  // CHECK:      "tf.EnqueueTPUEmbeddingArbitraryTensorBatch"
  // CHECK-SAME:   device_ordinal = 1
  // CHECK:      "tf.EnqueueTPUEmbeddingArbitraryTensorBatch"
  // CHECK-SAME:   device_ordinal = 0
  // CHECK:      "tf.EnqueueTPUEmbeddingArbitraryTensorBatch"
  // CHECK-SAME:   device_ordinal = 1
}

// -----