docstr: Compute the total loss, validate it, and return it.
  
  Subclasses can optionally override this method to provide custom loss
  computation logic.
  
  Example:
  
  ```python
  class MyModel(Model):
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
          self.loss_tracker = metrics.Mean(name='loss')
  
      def compute_loss(self, x, y, y_pred, sample_weight):
          loss = ops.means((y_pred - y) ** 2)
          loss += ops.sum(self.losses)
          self.loss_tracker.update_state(loss)
          return loss
  
      def reset_metrics(self):
          self.loss_tracker.reset_state()
  
      @property
      def metrics(self):
          return [self.loss_tracker]
  
  inputs = layers.Input(shape=(10,), name='my_input')
  outputs = layers.Dense(10)(inputs)
  model = MyModel(inputs, outputs)
  model.add_loss(ops.sum(outputs))
  
  optimizer = SGD()
  model.compile(optimizer, loss='mse', steps_per_execution=10)
  dataset = ...
  model.fit(dataset, epochs=2, steps_per_epoch=10)
  print(f"Custom loss: {model.loss_tracker.result()}")
  ```
  
  Args:
      x: Input data.
      y: Target data.
      y_pred: Predictions returned by the model (output of `model(x)`)
      sample_weight: Sample weights for weighting the loss function.
      allow_empty: If `False`, the method will error out if
          no loss has been computed by the model. If `True`, then
          if no loss is computed, the method returns 0.
  
  Returns:
      The total loss as a scalar tensor, or `None` if no loss results
      (which is the case when called by `Model.test_step`).
code: def compute_loss(
      self,
      x=None,
      y=None,
      y_pred=None,
      sample_weight=None,
      allow_empty=False,
  ):
      """Compute the total loss, validate it, and return it.
  
      Subclasses can optionally override this method to provide custom loss
      computation logic.
  
      Example:
  
      ```python
      class MyModel(Model):
          def __init__(self, *args, **kwargs):
              super().__init__(*args, **kwargs)
              self.loss_tracker = metrics.Mean(name='loss')
  
          def compute_loss(self, x, y, y_pred, sample_weight):
              loss = ops.means((y_pred - y) ** 2)
              loss += ops.sum(self.losses)
              self.loss_tracker.update_state(loss)
              return loss
  
          def reset_metrics(self):
              self.loss_tracker.reset_state()
  
          @property
          def metrics(self):
              return [self.loss_tracker]
  
      inputs = layers.Input(shape=(10,), name='my_input')
      outputs = layers.Dense(10)(inputs)
      model = MyModel(inputs, outputs)
      model.add_loss(ops.sum(outputs))
  
      optimizer = SGD()
      model.compile(optimizer, loss='mse', steps_per_execution=10)
      dataset = ...
      model.fit(dataset, epochs=2, steps_per_epoch=10)
      print(f"Custom loss: {model.loss_tracker.result()}")
      ```
  
      Args:
          x: Input data.
          y: Target data.
          y_pred: Predictions returned by the model (output of `model(x)`)
          sample_weight: Sample weights for weighting the loss function.
          allow_empty: If `False`, the method will error out if
              no loss has been computed by the model. If `True`, then
              if no loss is computed, the method returns 0.
  
      Returns:
          The total loss as a scalar tensor, or `None` if no loss results
          (which is the case when called by `Model.test_step`).
      """
      del x  # The default implementation does not use `x`.
      losses = []
      if self._compile_loss is not None:
          loss = self._compile_loss(y, y_pred, sample_weight)
          if loss is not None:
              losses.append(loss)
      for loss in self.losses:
          losses.append(ops.cast(loss, dtype=backend.floatx()))
      if not allow_empty and len(losses) == 0:
          raise ValueError(
              "No loss to compute. Provide a `loss` argument in `compile()`."
          )
      if len(losses) == 1:
          total_loss = losses[0]
      elif len(losses) == 0:
          total_loss = ops.zeros(())
      else:
          total_loss = ops.sum(losses)
      return total_loss
  
imports: import platform
  import warnings
  import tree
  from keras import backend
  from keras import metrics
  from keras import ops
  from keras import optimizers
  from keras.optimizers.loss_scale_optimizer import LossScaleOptimizer
  from keras.saving import serialization_lib
  from keras.trainers.compile_utils import CompileLoss
  from keras.trainers.compile_utils import CompileMetrics
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.utils import traceback_utils
  from keras.utils import tracking
label_desc: Use image node_id 12 for calling the Trainer obj's underlying member method code with example usage: obj.compute_loss(x, y, y_pred, sample_weight, allow_empty) and returns: total_loss