// CHECK: [[var:%.*]] = tf_mlrt.executeop.device([[device]]){{.*}}op: \22ResourceGather\22
  // CHECK: [[rendezvous_key_base:%.*]] = tf_mlrt_tpu.compile_and_execute([[var]])
  %0 = "tf.ResourceGather"(%arg0, %indices) {__op_key = 0: i32, device = "/device:CPU:0"} : (tensor<!tf_type.resource<tensor<4xf32>>>, tensor<i32>) -> tensor<f32>

      ),
      (TF_AssignVariableOp $m_resource, $new_m, (CreateConstBoolAttrFalse)),
      (TF_AssignVariableOp $v_resource, $new_v, (CreateConstBoolAttrFalse))
    ]
  >;

// Pattern to decompose tf.ResourceGather into tf.ReadVariable and tf.GatherV2.
def DecomposeResourceGather : Pat<
  (TF_ResourceGatherOp:$old_result
    $resource, $indices, $batch_dims, $validate_indices),
  (TF_GatherV2Op:$dest

  // 2) Convert composite functions like lstm/rnns, along with proper function
  // inlining & dce.
  // 3) Lower static tensor list pass.

  // This decomposes resource ops like ResourceGather into read-variable op
  // followed by gather. This is used when the saved model import path is used
  // during which resources don't get frozen in the python layer.
  pass_manager->addNestedPass<mlir::func::FuncOp>(