class_docstr: Computes the crossentropy metric between the labels and predictions.
  
  Use this crossentropy metric when there are two or more label classes.
  It expects labels to be provided as integers. If you want to provide labels
  that are one-hot encoded, please use the `CategoricalCrossentropy`
  metric instead.
  
  There should be `num_classes` floating point values per feature for `y_pred`
  and a single floating point value per feature for `y_true`.
  
  Args:
      name: (Optional) string name of the metric instance.
      dtype: (Optional) data type of the metric result.
      from_logits: (Optional) Whether output is expected
          to be a logits tensor. By default, we consider that output
          encodes a probability distribution.
      axis: (Optional) Defaults to `-1`.
          The dimension along which entropy is computed.
  
  Examples:
  
  Standalone usage:
  
  >>> # y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]
  >>> # logits = log(y_pred)
  >>> # softmax = exp(logits) / sum(exp(logits), axis=-1)
  >>> # softmax = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]
  >>> # xent = -sum(y * log(softmax), 1)
  >>> # log(softmax) = [[-2.9957, -0.0513, -16.1181],
  >>> #                [-2.3026, -0.2231, -2.3026]]
  >>> # y_true * log(softmax) = [[0, -0.0513, 0], [0, 0, -2.3026]]
  >>> # xent = [0.0513, 2.3026]
  >>> # Reduced xent = (0.0513 + 2.3026) / 2
  >>> m = keras.metrics.SparseCategoricalCrossentropy()
  >>> m.update_state([1, 2],
  ...                [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
  >>> m.result()
  1.1769392
  
  >>> m.reset_state()
  >>> m.update_state([1, 2],
  ...                [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
  ...                sample_weight=np.array([0.3, 0.7]))
  >>> m.result()
  1.6271976
  
  Usage with `compile()` API:
  
  ```python
  model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[keras.metrics.SparseCategoricalCrossentropy()])
  ```
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code: def get_config(self):
      return {
          "name": self.name,
          "dtype": self.dtype,
          "from_logits": self.from_logits,
          "axis": self.axis,
      }
  
imports: from keras.api_export import keras_export
  from keras.losses.losses import binary_crossentropy
  from keras.losses.losses import categorical_crossentropy
  from keras.losses.losses import kl_divergence
  from keras.losses.losses import poisson
  from keras.losses.losses import sparse_categorical_crossentropy
  from keras.metrics import reduction_metrics
label_desc: Use image node_id 2 for calling the SparseCategoricalCrossentropy obj's underlying member method code with example usage: obj.get_config() and returns: dict