class_docstr: Calculates the mean of absolute error between labels and predictions.
  
  Formula: error = abs(label - prediction)
  
  The metric computes the mean of absolute error between labels and
  predictions. The labels and predictions should be floats.
class_docstr_tok: ["Calculates","the","mean","of","absolute","error","between","labels","and","predictions",".","Formula",":","error","=","abs","(","label","-","prediction",")","The","metric","computes","the","mean","of","absolute","error","between","labels","and","predictions",".","The","labels","and","predictions","should","be","floats","."]
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_ABSOLUTE_ERROR_NAME):
      """Initializes mean regression error metric.
  
      Args:
        name: The name of the metric.
      """
      super().__init__(
          metric_util.merge_per_key_computations(
              _mean_absolute_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 MeanAbsoluteError object from inherited base classes: metric_types with example: obj = MeanAbsoluteError(name)