code: def classifier(
      hidden,
      targets,
      n_targets,
      config,
      train=False,
      reuse=None,
      **kwargs,
  ):
      """
      A simple linear classifier.
  
      :param hidden: The output of the featurizer. [batch_size, embed_dim]
      :param targets: One hot encoded target ids. [batch_size, n_classes]
      :param n_targets: A python int containing the number of classes that the model should be learning to predict over.
      :param dropout_placeholder:
      :param config: A config object, containing all parameters for the featurizer.
      :param train: If this flag is true, dropout and losses are added to the graph.
      :param reuse: Should reuse be set within this scope.
      :param kwargs: Spare arguments.
      :return: dict containing:
          logits: The unnormalised log probabilities of each class.
          losses: The loss for the classifier.
      """
      with tf.compat.v1.variable_scope("classifier", reuse=reuse):
          hidden = dropout(hidden, config.clf_p_drop, train)
          clf_logits = perceptron(hidden, n_targets, config)
          if targets is None:
              clf_losses = None
          else:
              clf_losses = tf.nn.softmax_cross_entropy_with_logits(
                  logits=clf_logits, labels=tf.stop_gradient(targets)
              )
  
              clf_losses = _apply_class_weight(
                  clf_losses,
                  targets,
                  kwargs.get("class_weights"),
                  norm_grads=config.renorm_after_class_weights,
              )
  
          return {"logits": clf_logits, "losses": clf_losses}
  
imports: import functools
  import tensorflow
  from tensorflow_addons.text.crf import crf_log_likelihood
  from finetune.base_models.gpt.featurizer import attn, dropout, norm
  from finetune.util.shapes import shape_list, merge_leading_dims
  from finetune.optimizers.recompute_grads import recompute_grad
  from finetune.errors import FinetuneError
  from finetune.nn.activations import act_fns
  from finetune.nn.nn_utils import norm
  from tensorflow.python.framework import function
label_desc: Use image node_id 6 for calling a global function with example usage: classifier(hidden, targets, n_targets, config, train, reuse) and returns: dict