}
}
node {
  name: "ParseExample/ParseExampleV2"
  op: "ParseExampleV2"
  input: "input0"
  input: "ParseExample/ParseExampleV2/names"
  input: "ParseExample/ParseExampleV2/sparse_keys"
  input: "ParseExample/ParseExampleV2/dense_keys"
  input: "ParseExample/ParseExampleV2/ragged_keys"
  input: "ParseExample/Const"
  input: "ParseExample/Const_1"
  attr {
    key: "Tdense"
    value {
      list {

>;

// Removes `stablehlo.custom_call @Sharding`. Assumes this call always accepts
// one input. Other attributes are ignored.
def RemoveShardingCustomCall : Pat<
  (StableHLO_CustomCallOp (variadic $input0), $call_target_name, $_, $_, $_, $_, $_, $_, $_),
  (replaceWithValue $input0),

sign.input 9d61b19deffd5a60ba84:d75a980182b10ab7d54b::e5564300c360ac729086: 4ccd089b28ff96da9db6:3d4017c3e843895a92b7:72:92a009a9f0d4cab8720e: c5aa8df43f9f837bedb7:fc51cd8e6218a1a38da4:af82:6291d657deec24024827: 0d4a05b07352a5436e18:e61a185bcef2613a6c7c:cbc77b:d9868d52c2bebce5f3fa: 6df9340c138cc188b5fe:c0dac102c4533186e25d:5f4c8989:124f6fc6b0d100842769: b780381a65edf8b78f69:e253af0766804b869bb1:18b6bec097:b2fc46ad47af464478c1: 78ae9effe6f245e924a7:fbcfbfa40505d7f2be44:89010d855972:6ed629fc1d9ce9e14687:...

  }
  func.return
}

// CHECK:      [[INPUT_A:%.+]], {{%.+}} = tf_executor.island {
// CHECK-NEXT:   [[OP_A_OUTPUT:%.+]] = "tf.opA"([[ARG_0]])
// CHECK-NEXT:   tf_executor.yield [[OP_A_OUTPUT]] :
// CHECK:      [[ISLAND_0_OUTPUT:%.+]], {{%.+}} = tf_executor.island {
// CHECK-NEXT:   [[OP_B_OUTPUT:%.+]] = "tf.opB"([[INPUT_A]]) {_parallel_execution_ids = "p0:0"}
// CHECK:        tf_executor.yield [[OP_B_OUTPUT]] :

  func.return %r : tensor<i32>
}

// -----

// Test inputs to the child streams are merged to the parent streams

// CHECK-LABEL: func private @main_stream_1
// CHECK-SAME: ([[INPUT0:%.*]]: tensor<i32>, [[INPUT1:%.*]]: tensor<i32>
// CHECK: tf.Sub
// CHECK: tf.Sub
// CHECK: mlrt.async({{%.*}}, [[INPUT1]]

// CHECK-LABEL: func @main

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

node {
  name: "input0"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
}
versions {
  producer: 29
  min_consumer: 12
}

# Verify that functions from the library are properly imported.

# CHECK-LABEL:  func @main() {
# CHECK:    "tf.Placeholder"

  std::vector<TFE_TensorHandle*> compute_fn_inputs;
  TFE_TensorHandle* input_a = TestScalarTensorHandle(ctx, 2.0f);
  TFE_TensorHandle* input_b = TestScalarTensorHandle(ctx, 1.0f);
  compute_fn_inputs.push_back(input_a);
  compute_fn_inputs.push_back(input_b);

  TFE_Op* compute_fn_op = TF_ConcreteFunctionMakeCallOp(

      // CHECK-NEXT: op: "ParseExampleV2"
      // CHECK-NEXT: input: "input0"
      // CHECK-NEXT: input: "tf.Const{{_.*_3}}"
      // CHECK-NEXT: input: "tf.Const{{_.*_5}}"
      // CHECK-NEXT: input: "tf.Const{{_.*_2}}"
      // CHECK-NEXT: input: "tf.Const{{_.*_4}}"
      // CHECK-NEXT: input: "tf.Const"
      // CHECK-NEXT: input: "tf.Const{{_.*_1}}"
      // CHECK-NEXT: attr {
      // CHECK-NEXT:   key: "Tdense"

# Verify that we match correctly the input / output when they are scalar.

# CHECK-LABEL: func @main
# CHECK-SAME:  (%{{[a-z0-9]+}}: tensor<f32> {tf.device = "/device:CPU:0"})
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "out:1,out"

# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=4 -tf-input-data-types=DT_INT32 -tf-output-arrays=output %s -o - --output-mlir | FileCheck %s

node {
  name: "default"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 0
      }
    }
  }
}