class_docstr: This class replaces all the missing values with the mean of the non missing
  values.
  
  Careful this is not a 'causal' method in the sense that it leaks
  information about the furture in the imputation. You may prefer to use
  CausalMeanValueImputation instead.
class_docstr_tok: ["This","class","replaces","all","the","missing","values","with","the","mean","of","the","non","missing","values",".","Careful","this","is","not","a","'causal","'","method","in","the","sense","that","it","leaks","information","about","the","furture","in","the","imputation",".","You","may","prefer","to","use","CausalMeanValueImputation","instead","."]
code: def __call__(self, values: np.ndarray) -> np.ndarray:
      if len(values) == 1 or np.isnan(values).all():
          return DummyValueImputation()(values)
      nan_indices = np.where(np.isnan(values))
      values[nan_indices] = np.nanmean(values)
      return values
  
imports: from typing import List, Optional, Union, Type
  import numpy
  import pandas
  from gluonts.core.component import validated
  from gluonts.dataset.common import DataEntry
  from gluonts.dataset.field_names import FieldName
  from gluonts.time_feature import TimeFeature
  from ._base import MapTransformation, SimpleTransformation
label_desc: Use image node_id 1 for calling the MeanValueImputation obj's underlying member method code with example usage: obj.__call__(values) and returns: values, DummyValueImputation