code: def hinge_loss(
      labels,
      logits,
      weights=1.0,
      scope=None,
      loss_collection=ops.GraphKeys.LOSSES,
      reduction=Reduction.SUM_BY_NONZERO_WEIGHTS,
  ):
      """Adds a hinge loss to the training procedure.
  
      Args:
        labels: The ground truth output tensor. Its shape should match the shape of
          logits. The values of the tensor are expected to be 0.0 or 1.0. Internally
          the {0,1} labels are converted to {-1,1} when calculating the hinge loss.
        logits: The logits, a float tensor. Note that logits are assumed to be
          unbounded and 0-centered. A value > 0 (resp. < 0) is considered a positive
          (resp. negative) binary prediction.
        weights: Optional `Tensor` whose rank is either 0, or the same rank as
          `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
          be either `1`, or the same as the corresponding `losses` dimension).
        scope: The scope for the operations performed in computing the loss.
        loss_collection: collection to which the loss will be added.
        reduction: Type of reduction to apply to loss.
  
      Returns:
        Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same
        shape as `labels`; otherwise, it is scalar.
  
      Raises:
        ValueError: If the shapes of `logits` and `labels` don't match or
          if `labels` or `logits` is None.
  
      @compatibility(eager)
      The `loss_collection` argument is ignored when executing eagerly. Consider
      holding on to the return value or collecting losses via a `tf.keras.Model`.
      @end_compatibility
      """
      if labels is None:
          raise ValueError("Argument `labels` must not be None.")
      if logits is None:
          raise ValueError("Argument `logits` must not be None.")
      with ops.name_scope(
          scope, "hinge_loss", (logits, labels, weights)
      ) as scope:
          logits = math_ops.cast(logits, dtype=dtypes.float32)
          labels = math_ops.cast(labels, dtype=dtypes.float32)
          logits.get_shape().assert_is_compatible_with(
              labels.get_shape()
          )
          # We first need to convert binary labels to -1/1 labels (as floats).
          all_ones = array_ops.ones_like(labels)
          labels = math_ops.subtract(2 * labels, all_ones)
          losses = nn_ops.relu(
              math_ops.subtract(
                  all_ones, math_ops.multiply(labels, logits)
              )
          )
          return compute_weighted_loss(
              losses,
              weights,
              scope,
              loss_collection,
              reduction=reduction,
          )
  
imports: from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import confusion_matrix
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import weights_broadcast_ops
  from tensorflow.python.ops.losses import util
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.deprecation import deprecated_args
  from tensorflow.python.util.deprecation import deprecated_argument_lookup
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 7 for calling a global function with example usage: hinge_loss(labels, logits, weights, scope, loss_collection, reduction) and returns: compute_weighted_loss