// isn't a composite op. The following ops are explicitly skipped here because
  // their "no-op" expansion is known to cause problems in some cases.
  static const char* kOpsToSkip[] = {
      "IdentityOp",
      "NoOp",              // b/174596063
      "OptionalHasValue",  // b/173136483
      "OptionalGetValue",  // b/173136483
      "VarHandleOp",       // b/176819198
  };
  for (const char* skip : kOpsToSkip) {

# CHECK: return %[[IDENTITY]]#1, %[[IDENTITY]]#0


def Test():

  x = tf.constant(1.0, shape=(5, 3))
  y = tf.constant(1.0, shape=(3, 5))

  s = tf.matmul(x, y)
  t = tf.matmul(y, x)
  [t, s] = array_ops.identity_n([t, s])

  tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(x)
  tensor_info_y = tf.compat.v1.saved_model.utils.build_tensor_info(y)
  tensor_info_s = tf.compat.v1.saved_model.utils.build_tensor_info(s)

  return value.getDefiningOp();
}

// TODO(b/158596585): Replace this with a cost model analysis.
bool IsTrivialUnaryOperation(Operation* op) {
  return llvm::isa<TF::CastOp, TF::IdentityOp>(op);
}

// Adds outside compilation attributes to unary ops such as Identity/Cast ops
// at the head of TPU computation that is used only by other outside compiled

  %val1 = "tf.Identity"(%val0) : (tensor<96x!tf_type.f32ref>) -> tensor<96xf32>
  func.return
}

// -----

// Test case: Basic converting. '_class' attribute is at IdentityOp.

func.func @f() {
  // CHECK: "tf.VarHandleOp"
  // CHECK: "tf.ReadVariableOp"
  %val0 = "tf.VariableV2"() {container = "", device = "", shape = #tf_type.shape<96>, shared_name = ""} : () -> tensor<96x!tf_type.f32ref>

    for (auto resource_user : resource_users_to_visit) {
      add_resource_op_to_list(resource_user);

      // Account for pass-through identity ops.
      if (auto pass_through_identity =
              llvm::dyn_cast<TF::IdentityOp>(resource_user)) {
        for (auto identity_user :
             pass_through_identity.getOutput().getUsers()) {
          new_resource_users.emplace_back(identity_user);
        }
      }
    }

  }
}

// -----

// Test that the @tf_quant_save's ops are cloned to @main. When there are no
// __tf_file_prefix argument in @main, confirm that it is created and wired
// to the newly created `IdentityOp`.

module attributes {tf_saved_model.semantics} {
  func.func private @tf_quant__save(%arg: tensor<!tf_type.string>) -> () {
    tf_executor.graph {

// Return a matmul op multiplying `a` by `b`.
TFE_Op* MatMulOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b);

// Return an identity op.
TFE_Op* IdentityOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return a shape op fetching the shape of `a`.
TFE_Op* ShapeOp(TFE_Context* ctx, TFE_TensorHandle* a);

//   backprop such that dx = g(dy). In Python,
//
//   ```python
//   with tf.get_default_graph().gradient_override_map(
//       {'IdentityN': 'OverrideGradientWithG'}):
//     y, _ = identity_n([f(x), x])
//
//   @tf.RegisterGradient('OverrideGradientWithG')
//   def ApplyG(op, dy, _):
//     return [None, g(dy)]  # Do not backprop to f(x).
//   ```
Status IdentityN(AbstractContext* ctx,