}

func.func @"__inference_Dataset_map_<lambda>_80"(%arg0: tensor<*xi32>) -> tensor<*xi32> {
  %0 = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32>
  %1 = "tf.Mul"(%arg0, %0) {device = ""} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
  %2 = "tf.Identity"(%1) {device = ""} : (tensor<*xi32>) -> tensor<*xi32>
  func.return %2 : tensor<*xi32>
}

          "i64, ellipsis_mask = 0 : i64, end_mask = 11 : i64, new_axis_mask = "
          "4 : i64, shrink_axis_mask = 0 : i64}> {device = \"\"} : "
          "(tensor<*xf32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) "
          "-> tensor<*xf32>\nError code: ERROR_NEEDS_FLEX_OPS",
          ConverterErrorData::ERROR_NEEDS_FLEX_OPS, "tf.StridedSlice",
          mlir::FileLineColLoc::get(input_file_id, 4, 10))),
      1);

func.func @side_effect_free_user() -> tensor<2xi32> {
  // CHECK: [[handle:%.*]] = "tf.VarHandleOp"
  // CHECK-SAME: shared_name = "x"
  // CHECK: [[value0:%.*]] = "tf.ReadVariableOp"([[handle]])
  // CHECK: [[value1:%.*]] = "tf.ReadVariableOp"([[handle]])
  // CHECK: "tf.ConcatV2"([[value1]], [[value0]]
  // CHECK-SAME: (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<2xi32>

# CHECK-LABEL: func @main
# CHECK-SAME:  ([[VAL_0:%.*]]: tensor<1x8x8x2xi32>) -> (tensor<1x8x8x2xi32>, tensor<i32>, tensor<10xi32>)
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "unranked"
# CHECK-SAME:  outputs = "unranked,static,static_10"
# CHECK-DAG:         [[VAL_2:%.*]] = arith.constant dense<0> : tensor<10xi32>
# CHECK-DAG:         [[VAL_1:%.*]] = arith.constant dense<0> : tensor<i32>

module {
func.func @main(%arg0: tensor<21x32x32x128xf32>, %arg1: tensor<2xi32>) -> tensor<1x64x64x128xf32> {
  %0 = "tf.ResizeBilinear"(%arg0, %arg1) {align_corners = false, device = "", half_pixel_centers = true} : (tensor<21x32x32x128xf32>, tensor<2xi32>) -> tensor<1x64x64x128xf32>
  func.return %0 : tensor<1x64x64x128xf32>
}

  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
}
versions {
  producer: 27
}

# CHECK-LABEL: func @main
# CHECK-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<8xi32>) -> tensor<8xi32>
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "input"
# CHECK:         %[[GRAPH:[0-9]+]] = tf_executor.graph
# CHECK:           tf_executor.fetch %[[ARG_0]]

    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
versions {
  producer: 27
}

# CHECK-LABEL: func @main
# CHECK-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<8xi32> loc({{.*}})) -> tensor<8xi32>
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "arg"
# CHECK-SAME:  outputs = "arg"
# CHECK:         %[[GRAPH:[0-9]+]] = tf_executor.graph
# CHECK:           tf_executor.fetch %[[ARG_0]]

    tf_executor.graph {
      %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
      tf_executor.fetch
    }
    func.return
  }
}

// CHECK-NOT: node

// CHECK: library
// CHECK-NEXT: function
// CHECK-NEXT: signature
// CHECK-NEXT: name: "main"

      %s:2, %control2 = tf_executor.island wraps "tf.Split"(%c, %a) {num_split = 2 : i32} : (tensor<i32>, tensor<2xf32>) -> (tensor<1xf32>, tensor<1xf32>)
      tf_executor.fetch
    }
    func.return
  }

      %c = stablehlo.constant dense<40> : tensor<2xi32>
      %0 = "tf.UnconvertedOp"(%arg0, %c) {align_corners = false, half_pixel_centers = false} : (tensor<1x20x20x28xf32>, tensor<2xi32>) -> tensor<1x40x40x28xf32>
      func.return %0 : tensor<1x40x40x28xf32>
  }