code: def rnn_abstract_eval(
      x_aval,
      h_0_aval,
      c_0_aval,
      w_aval,
      seq_lengths_aval,
      input_size: int,
      hidden_size: int,
      num_layers: int,
      dropout: float,
      bidirectional: bool,
      cudnn_allow_tf32: bool,
  ):
      batch_size, max_seq_length = x_aval.shape[0], x_aval.shape[1]
      num_directions = 2 if bidirectional else 1
      output_shape = (
          batch_size,
          max_seq_length,
          num_directions * hidden_size,
      )
      output_aval = core.ShapedArray(output_shape, x_aval.dtype)
      (
          _,
          reserve_space_size,
      ) = gpu_rnn.compute_rnn_workspace_reserve_space_sizes(  # pytype: disable=attribute-error
          input_size,
          hidden_size,
          num_layers,
          batch_size,
          max_seq_length,
          dropout,
          bidirectional,
          cudnn_allow_tf32,
      )
      reserve_space_aval = core.ShapedArray(
          (reserve_space_size,), jnp.float32
      )
      return output_aval, h_0_aval, c_0_aval, reserve_space_aval
  
imports: from functools import partial
  import math
  from typing import Any
  import jax
  import numpy
  from jax._src import core
  from jax.interpreters import mlir
  from jax.interpreters import xla
  from jax._src.custom_derivatives import custom_vjp
  from jax._src.typing import Array, Shape
  from jax._src.lax import lax
  import jax.numpy
label_desc: Use image node_id 16 for calling a global function with example usage: rnn_abstract_eval(x_aval, h_0_aval, c_0_aval, w_aval, seq_lengths_aval, input_size, hidden_size, num_layers, dropout, bidirectional, cudnn_allow_tf32) and returns: output_aval,  h_0_aval,  c_0_aval,  reserve_space_aval