class_docstr: The ``MeanScaler`` computes a per-item scale according to the average
  absolute value over time of each item. The average is computed only among
  the observed values in the data tensor, as indicated by the second
  argument. Items with no observed data are assigned a scale based on the
  global average.
  
  Parameters
  ----------
  minimum_scale
      default scale that is used if the time series has only zeros.
code: def __init__(
      self,
      minimum_scale: float = 1e-10,
      default_scale: Optional[float] = None,
      *args,
      **kwargs,
  ):
      super().__init__(*args, **kwargs)
      self.minimum_scale = minimum_scale
      self.default_scale = default_scale
  
imports: from typing import Tuple, Optional
  import mxnet.ndarray
  from mxnet.gluon import nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
label_desc: Use image node_id 1 to create a new MeanScaler object from inherited base classes: Scaler with example: obj = MeanScaler(minimum_scale, default_scale)