// CHECK: %[[VAR_VAL:.*]] = "tfl.read_variable"(%[[RESOURCE]]) : (tensor<!tf_type.resource<tensor<1x10xf32>>>) -> tensor<1x10xf32>
  // CHECK: %[[ADD:.*]] = tfl.add %[[VAR_VAL]], %arg0 {fused_activation_function = "NONE"} : tensor<1x10xf32>
  // CHECK: "tfl.assign_variable"(%[[RESOURCE]], %[[ADD]]) : (tensor<!tf_type.resource<tensor<1x10xf32>>>, tensor<1x10xf32>) -> ()

// CHECK-LABEL: QuantizeReadVariable
func.func @QuantizeReadVariable() -> (tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>) {
  %1 = "tfl.var_handle"() : () -> tensor<!tf_type.resource>
  %2 = "tfl.read_variable"(%1) : (tensor<!tf_type.resource>) -> tensor<1x2x1x3xf32>
  %3 = "tfl.quantize"(%2) {qtype = tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>, volatile} : (tensor<1x2x1x3xf32>) -> tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>

// CHECK: tf_executor.graph
// CHECK: %[[VAR_HANDLE:.*]], {{.*}} = tf_executor.island wraps "tf.VarHandleOp"() <{{{.*shared_name = "var_0".*}}}>
// CHECK: %[[READ_VARIABLE:.*]], {{.*}} = tf_executor.island wraps "tf.ReadVariableOp"(%[[VAR_HANDLE]])
// CHECK-DAG: %[[CST_0:.*]], {{.*}} = tf_executor.island wraps "tf.Const"() <{{{.*value = dense<"var_0"> : tensor<1x!tf_type\.string>.*}}}>

// CHECK: func.func private @tf_quant__save(%[[ARG:.*]]: tensor<!tf_type.string>)
// CHECK: %[[VAR_HANDLE:.*]] = "tf.VarHandleOp"()
// CHECK-SAME: {{.*shared_name = "var_0".*}}
// CHECK: %[[READ_VARIABLE:.*]] = "tf.ReadVariableOp"(%[[VAR_HANDLE]]) : (tensor<!tf_type.resource<tensor<2xf32>>>) -> tensor<2xf32>
// CHECK-DAG: %[[CONST_0:.*]] = "tf.Const"() <{{{.*value = dense<"var_0"> : tensor<1x!tf_type.string>.*}}}>

  %0 = "tfl.var_handle"() {container = "", shared_name = "states"} : () -> tensor<!tf_type.resource<tensor<1x2x3xf32>>>
  %1 = "tfl.read_variable"(%0) : (tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<1x2x3xf32>

  REAL=134,
  COMPLEX_ABS=135,
  HASHTABLE = 136,
  HASHTABLE_FIND = 137,
  HASHTABLE_IMPORT = 138,
  HASHTABLE_SIZE = 139,
  REDUCE_ALL = 140,
  CONV_3D_TRANSPOSE = 141,
  VAR_HANDLE = 142,
  READ_VARIABLE = 143,
  ASSIGN_VARIABLE = 144,
}
// LINT.ThenChange(nnapi_linter/linter.proto)

// Options for the builtin operators.
union BuiltinOptions {
  Conv2DOptions,
  DepthwiseConv2DOptions,

  REAL=134,
  COMPLEX_ABS=135,
  HASHTABLE = 136,
  HASHTABLE_FIND = 137,
  HASHTABLE_IMPORT = 138,
  HASHTABLE_SIZE = 139,
  REDUCE_ALL = 140,
  CONV_3D_TRANSPOSE = 141,
  VAR_HANDLE = 142,
  READ_VARIABLE = 143,
  ASSIGN_VARIABLE = 144,
  BROADCAST_ARGS = 145,
  RANDOM_STANDARD_NORMAL = 146,
  BUCKETIZE = 147,
  RANDOM_UNIFORM = 148,
  MULTINOMIAL = 149,
  GELU = 150,
  DYNAMIC_UPDATE_SLICE = 151,

    TFL_TensorOf<[F32, F64, I1, UI8, I8, QI8, QUI8, I32, I64, QI16, Complex<F<32>>, Complex<F<64>>]>:$value
  );

  let results = (outs);
}

def TFL_ReadVariableOp : TFL_Op<"read_variable", []> {
  let summary = "Reads variable value.";

  let description = [{
Read variable data identified by 'resource_id'.
  }];

  let arguments = (ins
    TFL_ResourceTensor:$resource_id

    "REAL",
    "COMPLEX_ABS",
    "HASHTABLE",
    "HASHTABLE_FIND",
    "HASHTABLE_IMPORT",
    "HASHTABLE_SIZE",
    "REDUCE_ALL",
    "CONV_3D_TRANSPOSE",
    "VAR_HANDLE",
    "READ_VARIABLE",
    "ASSIGN_VARIABLE",
    "BROADCAST_ARGS",
    "RANDOM_STANDARD_NORMAL",
    "BUCKETIZE",
    "RANDOM_UNIFORM",
    "MULTINOMIAL",
    "GELU",
    "DYNAMIC_UPDATE_SLICE",