class_docstr: Computes the mean squared error between `y_true` and `y_pred`.
  
  Formula:
  
  ```python
  loss = mean(square(y_true - y_pred))
  ```
  
  Args:
      name: (Optional) string name of the metric instance.
      dtype: (Optional) data type of the metric result.
  
  Example:
  
  >>> m = keras.metrics.MeanSquaredError()
  >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
  >>> m.result()
  0.25
class_docstr_tok: ["Computes","the","mean","squared","error","between","`","y_true","`","and","`","y_pred","`",".","Formula",":","``","`","python","loss","=","mean","(","square","(","y_true","-","y_pred",")",")","``","`","Args",":","name",":","(","Optional",")","string","name","of","the","metric","instance",".","dtype",":","(","Optional",")","data","type","of","the","metric","result",".","Example",":",">",">",">","m","=","keras.metrics.MeanSquaredError","(",")",">",">",">","m.update_state","(","[","[","0",",","1","]",",","[","0",",","0","]","]",",","[","[","1",",","1","]",",","[","0",",","0","]","]",")",">",">",">","m.result","(",")","0.25"]
code: def get_config(self):
      return {"name": self.name, "dtype": self.dtype}
  
imports: import warnings
  from keras import initializers
  from keras import ops
  from keras.api_export import keras_export
  from keras.losses.loss import squeeze_to_same_rank
  from keras.losses.losses import log_cosh
  from keras.losses.losses import mean_absolute_error
  from keras.losses.losses import mean_absolute_percentage_error
  from keras.losses.losses import mean_squared_error
  from keras.losses.losses import mean_squared_logarithmic_error
  from keras.metrics import reduction_metrics
  from keras.utils.numerical_utils import normalize
label_desc: Use image node_id 2 for calling the MeanSquaredError obj's underlying member method code with example usage: obj.get_config() and returns: dict