code: def weighted_topk_correctly_classified(
      logits: jnp.ndarray,
      multi_hot_target: jnp.ndarray,
      weights: Optional[jnp.ndarray] = None,
      k: int = 5,
  ) -> jnp.ndarray:
      """Computes weighted number of correctly classified given the top k prediction.
  
      This computes the weighted number of correctly classified examples/pixels in a
      single, potentially padded minibatch, given the top-k prediction. In the
      multi-hot target case, the sample is considered correct when any of the top-k
      predictions matches any of the multi-hot targets. If the minibatch/inputs is
      padded (i.e., it contains null examples/pad pixels) it is assumed that weights
      is a binary mask where 0 indicates that the example/pixel is null/padded. We
      assume the trainer will aggregate and divide by number of
      samples.
  
      Args:
        logits: Output of model in shape [batch, ..., num_classes].
        multi_hot_target: Multi hot vector of shape [batch, ..., num_classes].
        weights: None or array of shape [batch, ...] (rank of one_hot_target -1).
        k: Number of top prediction to consider.
  
      Returns:
        The number of correctly classified examples in the given batch, given top
        k prediction.
      """
      if logits.ndim != multi_hot_target.ndim:
          raise ValueError(
              "Incorrect shapes. Got shape %s logits and %s one_hot_target"
              % (str(logits.shape), str(multi_hot_target.shape))
          )
      if k <= 0 or k > logits.shape[-1]:
          raise ValueError(
              "Incorrect k. k must be in [1,%s]" % str(logits.shape[-1])
          )
  
      topk_pred = jax.lax.top_k(logits, k)[1]
  
      num_classes = logits.shape[-1]
      multi_hot_pred = jnp.sum(
          jax.nn.one_hot(topk_pred, num_classes=num_classes), axis=-2
      )
      correct = jnp.any(
          multi_hot_pred * multi_hot_target, axis=-1, keepdims=True
      ).astype(jnp.float32)
  
      if weights is not None:
          correct = apply_weights(correct, weights)
  
      return correct.astype(jnp.int32)
  
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 6 for calling a global function with example usage: weighted_topk_correctly_classified(logits, multi_hot_target, weights, k) and returns: correct