code: def weighted_l1_loss(
      x: jnp.ndarray,
      y: jnp.ndarray,
      weights: Optional[jnp.ndarray] = None,
      reduction: Optional[str] = None,
  ) -> jnp.ndarray:
      """L1 loss with optional reduction specified.
  
      Args:
        x: Input array of any shape.
        y: Input array of shape broadcastable to that of x.
        weights: Weights to apply to the loss.
        reduction: Type of reduction, which is from [None, 'mean'].
  
      Returns:
        reduction(jnp.abs(x - y)). 'mean' reduction takes the global mean. To use
        customized normalization use 'none' reduction and scale loss in the caller.
      """
      abs_diff = jnp.abs(x - y)
      if weights is not None:
          abs_diff = apply_weights(abs_diff, weights)
      if not reduction:
          return abs_diff
      elif reduction == "mean":
          return abs_diff.mean()
  
imports: import functools
  from typing import Optional, Any, Tuple, Union
  import flax.linen
  import jax
  import jax.numpy
  import numpy
label_desc: Use image node_id 15 for calling a global function with example usage: weighted_l1_loss(x, y, weights, reduction) and returns: abs_diff, abs_diff