class_docstr: Marginal transformation that transforms the target via an empirical CDF to
  a standard gaussian as described here: https://arxiv.org/abs/1910.03002.
  
  To be used in conjunction with a multivariate gaussian to from a copula.
  Note that this transformation is currently intended for multivariate
  targets only.
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docstr: Apply truncation to the empirical CDF estimator to reduce variance as
  described here: https://arxiv.org/abs/0903.0649.
  
  Parameters
  ----------
  m
      Input empirical CDF value.
  
  Returns
  -------
  res
      Truncated empirical CDf value.
docstr_tok: ["Apply","truncation","to","the","empirical","CDF","estimator","to","reduce","variance","as","described","here",":","https",":","\/\/arxiv.org\/abs\/0903.0649",".","Parameters","--","--","--","--","--","m","Input","empirical","CDF","value",".","Returns","--","--","--","-","res","Truncated","empirical","CDf","value","."]
code: def winsorized_cutoff(m: float) -> float:
      """
      Apply truncation to the empirical CDF estimator to reduce variance as
      described here: https://arxiv.org/abs/0903.0649.
  
      Parameters
      ----------
      m
          Input empirical CDF value.
  
      Returns
      -------
      res
          Truncated empirical CDf value.
      """
      res = 1 / (4 * m**0.25 * np.sqrt(3.14 * np.log(m)))
      assert 0 < res < 1
      return res
  
imports: from typing import Callable, Iterator, List, Optional, Tuple, Type
  import numpy
  from toolz import valmap
  from gluonts.core.component import validated, tensor_to_numpy
  from gluonts.dataset.common import DataEntry
  from gluonts.exceptions import assert_data_error
  from ._base import FlatMapTransformation, MapTransformation, SimpleTransformation
label_desc: Use image node_id 11 for calling the CDFtoGaussianTransform obj's underlying member method code with example usage: obj.winsorized_cutoff(m) and returns: res