// CHECK: %[[quantized_gather:.*]] = "tf.PartitionedCall"(
// CHECK-SAME: f = @quantized_gather_hybrid_fn_0
// CHECK: %[[float_conv:.*]] = "tf.PartitionedCall"(%[[quantized_gather]]
// CHECK-SAME: f = @composite_conv2d_fn_2
// CHECK: %[[quantize:.*]] = "tf.PartitionedCall"(%[[float_conv]]
// CHECK-SAME: f = @quantize_i8
// CHECK: %[[quantized_conv:.*]] = "tf.PartitionedCall"(%[[quantize]]

    %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: f = @composite_matmul_fn_1}>
// CHECK-SAME: {_tfl_quant_trait = "fully_quantizable"
// CHECK: %[[UNQUANTIZED_OUTPUT:.*]] = "tf.MatMul"(%arg0, %arg0)
// CHECK: }

  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"
      }

    %weight = "tf.Const"() {value = dense<2.000000e+00> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
    %1 = "tf.PartitionedCall"(%arg0, %weight) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_conv2d_fn_1} : (tensor<1x2x2x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

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>

  // CHECK:      node {
  // CHECK-NEXT:   name: "tf.PartitionedCall"
  // CHECK-NEXT:   op: "PartitionedCall"
  // CHECK:        func {
  // CHECK:          name: "foo"
  // CHECK:        }