// CHECK-SAME: ([[ARG0:%.*]]: tensor<i32>)
func.func @testTransitiveIdentity(%arg0: tensor<i32>) {
  // CHECK:      "tf_device.cluster"
  // CHECK:      "tf.PartitionedCall"([[ARG0]])
  // CHECK-SAME: f = @callee0
  %0 = "tf_device.cluster"() ({
    %1 = "tf.PartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @callee0} : (tensor<i32>) -> tensor<i32>
    tf_device.return %1 : tensor<i32>
  }) : () -> tensor<i32>

module {
// CHECK-LABEL: func @func0
  func.func @func0(%arg0: tensor<i1>) -> tensor<f32> {
    %0 = tf_executor.graph {
// CHECK-NOT: PartitionedCall
// CHECK: "tf.opA"
      %outputs, %control = tf_executor.island wraps "tf.PartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @_tpu_v1_compat_outlined::@_tpu_v1_compat_outlined_func0} : (tensor<i1>) -> tensor<i1>

func.func @main() {
  tf_executor.graph {
    // CHECK: node {
    // CHECK-NEXT: name: "tf.PartitionedCall"
    // CHECK-NEXT: op: "PartitionedCall"
    // CHECK:   func {
    // CHECK:     name: "foo"
    // CHECK:   }
    // CHECK: }
    %0 = tf_executor.island wraps "tf.PartitionedCall"() {Tin = [], Tout = [], config = "", config_proto = "", device = "", executor_type = "", f = @foo, name = "Call_foo"} : () -> ()

    %cst = "tf.Const"() {value = dense<127> : tensor<2x1024xi8>} : () -> tensor<2x1024xi8>
    %0 = "tf.Identity"(%cst) : (tensor<2x1024xi8>) -> tensor<2x1024xi8>
    %1 = "tf.PartitionedCall"(%0) {config = "", config_proto = "", executor_type = "", f = @composite_dequantize_uniform} : (tensor<2x1024xi8>) -> tensor<2x1024xbf16>

    %0:2 = tf_executor.graph {

  func.return %out_1, %out_2 : tensor<*xf32>, tensor<*xf32>

// CHECK-DAG: %[[CONST:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<512x512xf32>}> : () -> tensor<512x512xf32>
// CHECK: %[[PARTITIONEDCALL:.*]] = "tf.PartitionedCall"(%arg0, %[[CONST]])
// CHECK-SAME: <{config = "",
// CHECK-SAME: f = @composite_matmul_fn_1}>
// CHECK-NOT: {_tfl_quant_trait = "fully_quantizable"

node {
  name: "PartitionedCall"
  op: "PartitionedCall"
  attr {
    key: "Tin"
    value {
      list {
      }
    }
  }
  attr {
    key: "Tout"
    value {
      list {
        type: DT_UINT8
      }
    }
  }
  attr {
    key: "_gradient_op_type"
    value {
      s: "PartitionedCall-15"
    }
  }
  attr {
    key: "config"
    value {

    }
    ret {
      key: "identity"
      value: "Identity:output:0"
    }
  }
}
versions {
  producer: 29
  min_consumer: 12
}

# Find PartitionedCall ops in main and match the callee name.
# CHECK: func @main
# CHECK: "tf.PartitionedCall"
# CHECK-SAME: f = @[[FUNCTION_FOO:[a-zA-Z0-9_]*]]

# Find callee and verify it has the stateful attribute set.
# CHECK: func private @[[FUNCTION_FOO]]

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -o - | FileCheck %s

node {
  name: "_tf.PartitionedCall"
  op: "PartitionedCall"
  attr {
    key: "Tin"
    value {
      list {
      }
    }
  }
  attr {
    key: "Tout"
    value {
      list {
        type: DT_FLOAT
      }
    }
  }
  attr {
    key: "f"
    value {
      func {
        name: "foo"
      }

func.func @dont_inline_custom_on_duplicated_cases() -> tensor<2xi32> {
  // CHECK-NEXT: "tf.PartitionedCall"
  // CHECK-NEXT: "tf.PartitionedCall"
  // CHECK-NEXT: return
  %0 = "tf.PartitionedCall"() {config = "", config_proto = "", executor_type = "", f = @custom_callee} : () -> tensor<2xi32>
  %1 = "tf.PartitionedCall"() {config = "", config_proto = "", executor_type = "", f = @custom_callee} : () -> tensor<2xi32>