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","."]
docstr: Finds the indices of the active piece-wise linear function.
  
  Parameters
  ----------
  sorted_vec
      Sorted target vector.
  to_insert_vec
      Vector for which the indicies of the active linear functions
      will be computed
  
  Returns
  -------
  indices
      Indices mapping to the active linear function.
docstr_tok: ["Finds","the","indices","of","the","active","piece-wise","linear","function",".","Parameters","--","--","--","--","--","sorted_vec","Sorted","target","vector",".","to_insert_vec","Vector","for","which","the","indicies","of","the","active","linear","functions","will","be","computed","Returns","--","--","--","-","indices","Indices","mapping","to","the","active","linear","function","."]
code: def _search_sorted(
      sorted_vec: np.ndarray, to_insert_vec: np.ndarray
  ) -> np.ndarray:
      """
      Finds the indices of the active piece-wise linear function.
  
      Parameters
      ----------
      sorted_vec
          Sorted target vector.
      to_insert_vec
          Vector for which the indicies of the active linear functions
          will be computed
  
      Returns
      -------
      indices
          Indices mapping to the active linear function.
      """
      indices_left = np.searchsorted(
          sorted_vec, to_insert_vec, side="left"
      )
      indices_right = np.searchsorted(
          sorted_vec, to_insert_vec, side="right"
      )
  
      indices = indices_left + (indices_right - indices_left) // 2
      indices = indices - 1
      indices = np.minimum(indices, len(sorted_vec) - 1)
      indices[indices < 0] = 0
      return indices
  
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 7 for calling the CDFtoGaussianTransform obj's underlying member method code with example usage: obj._search_sorted(sorted_vec, to_insert_vec) and returns: indices