class_docstr: Loss base class.
  
  To be implemented by subclasses:
  
  * `call()`: Contains the logic for loss calculation using `y_true`,
      `y_pred`.
  
  Example subclass implementation:
  
  ```python
  class MeanSquaredError(Loss):
      def call(self, y_true, y_pred):
          return ops.mean(ops.square(y_pred - y_true), axis=-1)
  ```
class_docstr_tok: ["Loss","base","class",".","To","be","implemented","by","subclasses",":","*","`","call","(",")","`",":","Contains","the","logic","for","loss","calculation","using","`","y_true","`",",","`","y_pred","`",".","Example","subclass","implementation",":","``","`","python","class","MeanSquaredError","(","Loss",")",":","def","call","(","self",",","y_true",",","y_pred",")",":","return","ops.mean","(","ops.square","(","y_pred","-","y_true",")",",","axis=-1",")","``","`"]
code: def call(self, y_true, y_pred):
      raise NotImplementedError
  
file_path: keras/keras/losses/loss.py
imports: import tree
  from keras import backend
  from keras import ops
  from keras.api_export import keras_export
  from keras.utils.naming import auto_name
label_desc: Use image node_id 3 for calling the Loss obj's underlying member method code with example usage: obj.call(y_true, y_pred) without return types