func.func @main(%arg0: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "input0", outputs = "output0"}} {
  %graph = tf_executor.graph {
    tf_executor.fetch %arg0 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK:      node {
// CHECK-NEXT:   name: "input0"
// CHECK-NEXT:   op: "_Arg"
// CHECK:          key: "T"
// CHECK-NEXT:     value {

  %10 = "tf.opK"() : () -> tensor<*xi16>
  %11 = "tf.opL"() : () -> tensor<*xi64>
  tf_device.replicate([%0, %1, %2] as %input0: tensor<*xi1>, %9 as %input1: tensor<*xi8>, %10 as %input2: tensor<*xi16>, [%3, %4, %5] as %input3: tensor<*xi32>, [%6, %7, %8] as %input4: tensor<*xf32>, %11 as %input5: tensor<*xi64>) {n = 3 : i32} {
    tf_device.return
  }
  func.return

// CHECK:      %[[OP_A:[a-z0-9]*]] = "tf.opA"

  }
}
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),

  // CHECK: %[[INPUT0:.*]] = "tf.Placeholder.input"
  %0 = "tf.Placeholder.input"(%arg0) : (tensor<f32>) -> tensor<f32>
  // CHECK: %[[INPUT1:.*]] = "tf.Placeholder.input"
  %1 = "tf.Placeholder.input"(%arg1) : (tensor<f32>) -> tensor<f32>
  %2 = arith.constant dense<true> : tensor<i1>

  // CHECK: "tf.Multiply"(%[[INPUT1]], %[[INPUT0]])

// RUN: tac-translate -input-mlir -output-mlir -device-specs=GPU %s -o - 2>&1 | FileCheck %s

module {
func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> attributes {tf.entry_function = {inputs = "input0,input1,input2,input3", outputs = "output"}} {
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

node {
  name: "replicated_input_0"
  op: "Identity"
  input: "input0"
  input: "^TPUReplicateMetadata"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "_tpu_replicate"
    value {
      s: "cluster"
    }
  }
}
node {
  name: "replicated_input_1"
  op: "Identity"
  input: "input1"
  input: "^TPUReplicateMetadata"
  attr {
    key: "T"

      // 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"

# 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"

      %outputs_5, %control_6 = tf_executor.island wraps "tf.TPUReplicatedInput"(%outputs_3) {N = 1 : i64, T = "tfdtype$DT_FLOAT", device = "", name = "input0"} : (tensor<0xf32>) -> tensor<0xf32>