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.
class_docstr_tok: ["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","."]
code: def map_transform(self, data: DataEntry, is_train: bool) -> DataEntry:
      self._preprocess_data(data, is_train=is_train)
      self._calc_pw_linear_params(data)
  
      for target_field in [
          self.past_target_field,
          self.future_target_field,
      ]:
          data[
              target_field + self.cdf_suffix
          ] = self.standard_gaussian_ppf(
              self._empirical_cdf_forward_transform(
                  data[self.sort_target_field],
                  data[target_field],
                  data[self.slopes_field],
                  data[self.intercepts_field],
              )
          )
      return data
  
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 2 for calling the CDFtoGaussianTransform obj's underlying member method code with example usage: obj.map_transform(data, is_train) and returns: data