code: def ordinal_regressor(
      hidden,
      targets,
      n_targets,
      config,
      shared_threshold_weights=True,
      train=False,
      reuse=None,
      **kwargs,
  ):
      """
      Ordinal Regressor using all-threshold loss.
  
      :param hidden: The output of the featurizer. [batch_size, embed_dim]
      :param targets: The placeholder representing the regression targets (binary threshold values). [batch_size]
      :param n_targets: A python int containing the number of thresholds 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: All-threshold Loss for the regression targets.
      """
      with tf.compat.v1.variable_scope("ordinalregressor", reuse=reuse):
          hidden = dropout(hidden, config.clf_p_drop, train)
          if shared_threshold_weights:
              w_init = tf.compat.v1.random_normal_initializer(
                  stddev=config.weight_stddev
              )
              b_init = tf.compat.v1.random_normal_initializer(0)
              nx = config.n_embed
              w = tf.compat.v1.get_variable(
                  "w", [nx, 1], initializer=w_init
              )
              b = tf.compat.v1.get_variable(
                  "b", [n_targets], initializer=b_init
              )
              logits = tf.matmul(hidden, w) + b
          else:
              logits = perceptron(hidden, n_targets, config)
  
          if targets is None:
              outputs = tf.sigmoid(logits)
              loss = None
          else:
              outputs = logits
              loss = tf.nn.sigmoid_cross_entropy_with_logits(
                  logits=logits, labels=tf.stop_gradient(targets)
              )
          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 10 for calling a global function with example usage: ordinal_regressor(hidden, targets, n_targets, config, shared_threshold_weights, train, reuse) and returns: dict