code: def flip_sequences(inputs: Array, lengths: Array) -> Array:
      """Flips a sequence of inputs along the time dimension.
  
      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.
      """
      # Note: since this function is vmapped, the code below is effectively for
      # a single example.
      max_length = inputs.shape[0]
      return jnp.flip(
          jnp.roll(inputs, max_length - lengths, axis=0), axis=0
      )
  
imports: import functools
  from typing import Any, Callable, Optional
  from flax import linen
  import jax
  from jax import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: flip_sequences(inputs, lengths) and returns: jnp