code: def rnn(
      step_function,
      inputs,
      initial_states,
      /,
      *,
      go_backwards: bool = False,
      mask: Optional[Union[tf.Tensor, tf.Variable]] = None,
      constants: Optional[Union[tf.Tensor, tf.Variable]] = None,
      unroll: bool = False,
      input_length: Optional[int] = None,
      time_major: bool = False,
      zero_output_for_mask: bool = False,
      return_all_outputs: bool = True,
  ):
      step_function = inputs_to_ivy_arrays(
          output_to_native_arrays(step_function)
      )
      return tf.keras.backend.rnn(
          step_function,
          inputs,
          initial_states,
          go_backwards=go_backwards,
          mask=mask,
          constants=constants,
          unroll=unroll,
          input_length=input_length,
          time_major=time_major,
          zero_output_for_mask=zero_output_for_mask,
          return_all_outputs=return_all_outputs,
      )
  
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 50 for calling a global function with example usage: rnn() and returns: tf