code: def regressor(
      hidden,
      targets,
      n_targets,
      config,
      train=False,
      reuse=None,
      **kwargs,
  ):
      """
      A simple linear regressor.
  
      :param hidden: The output of the featurizer. [batch_size, embed_dim]
      :param targets: The placeholder representing the regression targets. [batch_size]
      :param n_targets: A python int containing the number of outputs 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 regression outputs.
          losses: L2 Loss for the regression targets.
      """
      with tf.compat.v1.variable_scope("regressor", reuse=reuse):
          hidden = dropout(hidden, config.clf_p_drop, train)
          outputs = perceptron(hidden, n_targets, config)
          if targets is None:
              loss = None
          else:
              if config.regression_loss.upper() == "L2":
                  loss = tf.nn.l2_loss(outputs - targets)
              elif config.regression_loss.upper() == "L1":
                  loss = tf.abs(outputs - targets)
              else:
                  raise FinetuneError(
                      "regression_loss needs to be either L1 or L2, instead it is {}".format(
                          config.regression_loss
                      )
                  )
          return {"logits": outputs, "losses": loss}
  
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 9 for calling a global function with example usage: regressor(hidden, targets, n_targets, config, train, reuse) and returns: dict