// CHECK: [[OLD_VAR:%.*]] = "tf.ReadVariableOp"([[VAR_HANDLE]]) : (tensor<*x!tf_type.resource<tensor<*xf32>>>) -> tensor<*xf32>
    // CHECK: [[NEW_VAR:%.*]] = "tf.Sub"([[OLD_VAR]], [[VAR_DELTA]])
    // CHECK: "tf.AssignVariableOp"([[VAR_HANDLE]], [[NEW_VAR]])
    // CHECK: "tf.AssignVariableOp"([[M_HANDLE]], [[NEW_M]])
    // CHECK: "tf.AssignVariableOp"([[V_HANDLE]], [[NEW_V]])

  %0 = "tfl.var_handle"() : () -> tensor<!tf_type.resource>
  %1 = "tfl.dequantize"(%arg0) : (tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>) -> tensor<1x2x1x3xf32>
  "tfl.assign_variable"(%0, %1) : (tensor<!tf_type.resource>, tensor<1x2x1x3xf32>) -> ()
  func.return %arg0 : tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>

Empty strings indicate that they are non-partitioned tensors.}]>:$shape_and_slices,
    // VarHandles for restored tensors.
    Variadic<TF_Tensor>:$var_handles,
    // Data types for restored tensor.
    TypeArrayAttr: $restored_dtypes,
    // whether trucate is used when casting from the restored_dytpe to the type specified in VarHandle
    DenseBoolArrayAttr: $truncate_in_cast
  );

}

                                  strlen(ResourceHandle::ANONYMOUS_NAME)));

  AbstractTensorHandle* var_handle = nullptr;
  int num_retvals = 1;
  TF_RETURN_IF_ERROR(varhandle_op->Execute(
      absl::MakeSpan(&var_handle, num_retvals), &num_retvals));
  AbstractTensorHandlePtr owned_var_handle(var_handle);
  if (!tensorflow::isa<ImmediateExecutionTensorHandle>(
          owned_var_handle.get())) {

    func.return %2, %3 : tensor<1x10xf32>, tensor<1x10xi64>
  }

  // CHECK: %[[RESOURCE:.*]] = "tfl.var_handle"() <{container = "c", shared_name = "a"}> : () -> tensor<!tf_type.resource<tensor<1x10xf32>>>
  // CHECK: %[[RESOURCE_1:.*]] = "tfl.var_handle"() <{container = "c", shared_name = "b"}> : () -> tensor<!tf_type.resource<tensor<1x10xi64>>>

// CHECK-NEXT: return %[[q]]
}

// CHECK-LABEL: NotQuantizeReadVariable
func.func @NotQuantizeReadVariable() -> tensor<1x2x3x!quant.uniform<u8<1:255>:f32, 0.047244094488188976:128>> {
  %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>

  IMAG=133,
  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,

  IMAG=133,
  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,

//
// It converts (VariableV2 -> Identity) to (VarHandle -> ReadVariable).
//
// For the background, this pass is a part of hoisting VariableV2 ops by
// re-using the pipeline for hoisting (VarHandle -> ReadVariable) cases, which
//  can be done by the following passes:
//  - Capturing resource values into global tensors (importing saved model).
//  - Promoting VarHandle ops to function input/outputs.
//  - Freezing global tensor pass.

  let arguments = (ins
    TFL_FpTensor:$input,
    TFL_I32Tensor:$fft_length
  );

  let results = (outs
    TFL_Complex64Tensor:$output
  );
}

def TFL_VarHandleOp : TFL_Op<"var_handle", []> {
  let summary = "Returns a handle to a variable resource from its name.";

  let description = [{
    Returns a handle for a variable resource from its name.