code: def flip_sequences(
      inputs: Array,
      seq_lengths: Optional[Array],
      num_batch_dims: int,
      time_major: bool,
  ) -> Array:
      """Flips a sequence of inputs along the time axis.
  
      This function can be used to prepare inputs for the reverse direction of a
      bidirectional LSTM. It solves the issue that, when naively flipping multiple
      padded sequences stored in a matrix, the first elements would be padding
      values for those sequences that were padded. This function keeps the padding
      at the end, while flipping the rest of the elements.
  
      Example:
      ```python
      inputs = [[1, 0, 0],
                [2, 3, 0]
                [4, 5, 6]]
      lengths = [1, 2, 3]
      flip_sequences(inputs, lengths) = [[1, 0, 0],
                                         [3, 2, 0],
                                         [6, 5, 4]]
      ```
  
      Args:
        inputs: An array of input IDs <int>[batch_size, seq_length].
        lengths: The length of each sequence <int>[batch_size].
  
      Returns:
        An ndarray with the flipped inputs.
      """
      # Compute the indices to put the inputs in flipped order as per above example.
      time_axis = 0 if time_major else num_batch_dims
      max_steps = inputs.shape[time_axis]
  
      if seq_lengths is None:
          # reverse inputs and return
          inputs = jnp.flip(inputs, axis=time_axis)
          return inputs
  
      seq_lengths = jnp.expand_dims(seq_lengths, axis=time_axis)
  
      # create indexes
      idxs = jnp.arange(max_steps - 1, -1, -1)  # [max_steps]
      if time_major:
          idxs = jnp.reshape(idxs, [max_steps] + [1] * num_batch_dims)
      else:
          idxs = jnp.reshape(
              idxs, [1] * num_batch_dims + [max_steps]
          )  # [1, ..., max_steps]
      idxs = (idxs + seq_lengths) % max_steps  # [*batch, max_steps]
      idxs = _expand_dims_like(
          idxs, target=inputs
      )  # [*batch, max_steps, *features]
      # Select the inputs in flipped order.
      outputs = jnp.take_along_axis(inputs, idxs, axis=time_axis)
  
      return outputs
  
imports: from functools import partial
  from typing import Any, Callable, Dict, Mapping, Optional, Sequence, Tuple, TypeVar, Union
  import jax
  import numpy
  from absl import logging
  from jax import numpy
  from jax import random
  from typing_extensions import Protocol
  from flax.core import lift
  from flax.core.frozen_dict import FrozenDict
  from flax.linen import initializers, transforms
  from flax.linen.activation import sigmoid, tanh
  from flax.linen.dtypes import promote_dtype
  from flax.linen.linear import Conv, Dense, PrecisionLike, default_kernel_init
  from flax.linen.module import Module, compact, nowrap
label_desc: Use image node_id 3 for calling a global function with example usage: flip_sequences(inputs, seq_lengths, num_batch_dims, time_major) and returns: outputs, inputs