code: def hinge_embedding_loss(
      input: tf.Tensor,
      target: tf.Tensor,
      *,
      margin: float = 1.0,
      reduction: str = "mean",
  ) -> tf.Tensor:
      zero_ = tf.zeros([1], dtype=input.dtype)
  
      relu_part = tf.math.maximum(margin - input, 0)
  
      loss = tf.where(tf.equal(target, 1.0), input, zero_) + tf.where(
          tf.equal(target, -1.0), relu_part, zero_
      )
  
      return _apply_loss_reduction(loss, reduction)
  
file_path: ivy/ivy/functional/backends/tensorflow/experimental/losses.py
imports: import tensorflow
  import math
  from typing import Optional
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_device_and_dtypes
  from .None import backend_version
label_desc: Use image node_id 7 for calling a global function with example usage: hinge_embedding_loss(input, target) and returns: _apply_loss_reduction