code: def weighted_box_l1_loss(
      pred: jnp.ndarray,
      tgt: jnp.ndarray,
      weights: Optional[jnp.ndarray] = None,
      reduction: Optional[str] = None,
      tight: bool = True,
  ) -> jnp.ndarray:
      """L1 loss for bounding box with optional reduction specified.
  
      Args:
        pred: Prediction boxes of shape (..., 4), where the last dimension has form
          (x_min, y_min, x_max, y_max).
        tgt: Target boxes of shape (..., 4), where the last dimension has form
          (x_min, y_min, x_max, y_max).
        weights: Weights to apply to the loss.
        reduction: Type of reduction, which is from [None, 'mean'].
        tight: If True, returns the vanilla L1 loss on the bounding box coordinates.
          If False, returns loose bounding-box L1 loss, where prediction edges only
          generate loss when they stretch outside the target box, but not when they
          are within it.
  
      Returns:
        reduction(jnp.abs(src - tgt)). 'mean' reduction takes the global mean. To
        use customized normalization use 'none' reduction and scale loss in the
        caller.
      """
      if pred.shape[-1] != 4:
          raise ValueError(
              f"The last dimension of the prediction boxes must be 4."
              f" Got shape {pred.shape}."
          )
      if tgt.shape[-1] != 4:
          raise ValueError(
              f"The last dimension of the target boxes must be 4."
              f" Got shape {tgt.shape}."
          )
      if tight:
          abs_diff = jnp.abs(pred - tgt)
      else:
          xy1, xy2 = jnp.split(pred - tgt, 2, axis=-1)
          xy1 = jnp.minimum(xy1, 0.0)
          xy2 = jnp.maximum(xy2, 0.0)
          abs_diff = jnp.abs(jnp.concatenate([xy1, xy2], axis=-1))
      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()
      else:
          raise ValueError(f"Unknown reduction: {reduction}")
  
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 16 for calling a global function with example usage: weighted_box_l1_loss(pred, tgt, weights, reduction, tight) and returns: abs_diff, abs_diff