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 standard_gaussian_ppf(y: np.ndarray) -> np.ndarray:
      y_clipped = np.clip(y, a_min=1.0e-6, a_max=1.0 - 1.0e-6)
      return np.sqrt(2.0) * erfinv(2.0 * y_clipped - 1.0)
  
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 10 for calling the CDFtoGaussianTransform obj's underlying member method code with example usage: obj.standard_gaussian_ppf(y) and returns: unknown