class_docstr: Calculates the mean of squared error between labels and predictions.
  
  Formula: error = L2_norm(label - prediction)**2
  
  The metric computes the mean of squared error (square of L2 norm) between
  labels and predictions. The labels and predictions could be arrays of
  arbitrary dimensions. Their dimension should match.
class_docstr_tok: ["Calculates","the","mean","of","squared","error","between","labels","and","predictions",".","Formula",":","error","=","L2_norm","(","label","-","prediction",")","*","*","2","The","metric","computes","the","mean","of","squared","error","(","square","of","L2","norm",")","between","labels","and","predictions",".","The","labels","and","predictions","could","be","arrays","of","arbitrary","dimensions",".","Their","dimension","should","match","."]
docstr: Initializes mean regression error metric.
  
  Args:
    name: The name of the metric.
docstr_tok: ["Initializes","mean","regression","error","metric",".","Args",":","name",":","The","name","of","the","metric","."]
code: def __init__(self, name: str = MEAN_SQUARED_ERROR_NAME):
      """Initializes mean regression error metric.
  
      Args:
        name: The name of the metric.
      """
      super().__init__(
          metric_util.merge_per_key_computations(
              _mean_squared_error_computations
          ),
          name=name,
      )
  
imports: import abc
  import dataclasses
  from typing import Iterable, Optional, Dict
  import apache_beam
  import numpy
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
label_desc: Use image node_id 1 to create a new MeanSquaredError object from inherited base classes: metric_types with example: obj = MeanSquaredError(name)