class_docstr: Computes the Poisson metric between `y_true` and `y_pred`.
  
  Formula:
  
  ```python
  metric = y_pred - y_true * log(y_pred)
  ```
  
  Args:
      name: (Optional) string name of the metric instance.
      dtype: (Optional) data type of the metric result.
  
  Examples:
  
  Standalone usage:
  
  >>> m = keras.metrics.Poisson()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
  >>> m.result()
  0.49999997
  
  >>> m.reset_state()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
  ...                sample_weight=[1, 0])
  >>> m.result()
  0.99999994
  
  Usage with `compile()` API:
  
  ```python
  model.compile(optimizer='sgd',
                loss='mse',
                metrics=[keras.metrics.Poisson()])
  ```
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code: def get_config(self):
      return {"name": self.name, "dtype": self.dtype}
  
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 Poisson obj's underlying member method code with example usage: obj.get_config() and returns: dict