// Check that attributes that define derived shapes are exported.

// CHECK: op: "PlaceholderWithDefault"
// CHECK: shape
// CHECK: unknown_rank: true
// CHECK: name: "static"
// CHECK: op: "PlaceholderWithDefault"
// CHECK: shape {
// CHECK-NEXT: }
// CHECK: name: "static_10"
// CHECK: op: "PlaceholderWithDefault"
// CHECK: shape
// CHECK: dim
// CHECK: size: 10

func.func @main() {
  tf_executor.graph {

      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 0
      }
    }
  }
  experimental_debug_info {
  }
}
node {
  name: "unranked"
  op: "PlaceholderWithDefault"
  input: "tf.Const1"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        unknown_rank: true
      }

Clustered nodes: 2725
Unclustered nodes: 606
Number of clusters: 2

unclustered size 606
 AssignAddVariableOp 1
 Const 108
 DivNoNan 1
 Identity 2
 Merge 53
 PlaceholderWithDefault 1
 ReadVariableOp 2
 Switch 1
 _Arg 435
 _Retval 2
cluster 0 size 1910
 Add 16
 AddN 71
 ArgMax 1
 AssignAddVariableOp 1
 BiasAdd 1
 BiasAddGrad 1
 Cast 115
 Const 407
 Conv2D 53
 Conv2DBackpropFilter 53

    "MatrixSetDiagV2",
    "MatrixSetDiagV3",
    "MirrorPad",
    "OneHot",
    "OnesLike",
    "Pack",
    "Pad",
    "PadV2",
    "ParallelConcat",
    "PlaceholderV2",
    "PlaceholderWithDefault",
    "PreventGradient",
    "QuantizeAndDequantize",
    "QuantizeV2",
    "Rank",
    "Reshape",
    "ResourceStridedSliceAssign",
    "ReverseSequence",
    "ReverseV2",

  // CHECK-LABEL: CheckNumerics
  // CHECK:  return %arg0 : tensor<3xf32>
}

func.func @placeholder_with_default(%arg0: tensor<3xf32>) -> tensor<3xf32> {
  %0 = "tf.PlaceholderWithDefault"(%arg0): (tensor<3xf32>) -> tensor<3xf32>
  func.return %0 : tensor<3xf32>
  // Should be converted to Identity and then from Identity to value
  // CHECK-LABEL: placeholder_with_default

  let arguments = (ins
  );

  let results = (outs
    TF_Tensor:$output
  );

  TF_DerivedResultTypeAttr dtype = TF_DerivedResultTypeAttr<0>;
}

def TF_PlaceholderWithDefaultOp : TF_Op<"PlaceholderWithDefault", [Pure]> {
  let summary = "Placeholder op";

  let description = [{
    A placeholder op that passes through input when its output is not fed.
  }];

  let arguments = (ins
    TF_Tensor:$input

            "_UnaryOpsComposition", "CollectiveReduceV2",
            "CollectiveAssignGroupV2",
            // The following 5 operations are converted to identity
            "PlaceholderWithDefault", "PreventGradient", "StopGradient",
            "Snapshot", "_EagerConst"}},
          // clang-format off
    {"RED",
     {"All", "Any", "Min", "Max", "Mean", "Prod", "Sum"}},
          // clang-format on

  // control dependencies are not to be executed. For single output nodes,
  // Placeholders can be converted to a NoOp if there are no uses, and
  // PlaceholderWithDefault can be converted to an Identity.
  for (const auto* edge : control_edges) {
    graph_->AddControlEdge(placeholder_node, edge->dst());
    graph_->RemoveControlEdge(edge);
  }

  if (update_inplace) {

  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "keras_learning_phase"
  op: "PlaceholderWithDefault"
  input: "keras_learning_phase/input"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_BOOL
    }
  }
  attr {