code: def rfft_operations(x, rank, norm_factor):
      if x.shape.rank == 1:
          x = tf.signal.rfft(x)
      elif x.shape.rank == 2:
          x = tf.switch_case(
              rank - 1,
              {
                  0: lambda: tf.signal.rfft(x),
                  1: lambda: tf.signal.rfft2d(x),
              },
          )
      else:
          x = tf.switch_case(
              rank - 1,
              {
                  0: lambda: tf.signal.rfft(x),
                  1: lambda: tf.signal.rfft2d(x),
                  2: lambda: tf.signal.rfft3d(x),
              },
          )
      norm_factor = tf.cast(norm_factor, tf.complex128)
      x = x / norm_factor
      x = tf.cast(x, tf.complex128)
      return x
  
imports: import math
  from typing import Union, Optional, Tuple, List, Literal, Sequence, Callable
  import tensorflow
  from ivy.func_wrapper import inputs_to_ivy_arrays, output_to_native_arrays, with_unsupported_dtypes, with_supported_dtypes, with_supported_device_and_dtypes
  from ..None import backend_version
  import ivy
  from ivy.functional.ivy.layers import _handle_padding, _get_num_padded_values, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _get_size
label_desc: Use image node_id 43 for calling a global function with example usage: rfft_operations(x, rank, norm_factor) and returns: x