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: Applies the forward transformation to the marginals of the multivariate
  target. Target (real valued) -> empirical cdf [0, 1]
  
  Parameters
  ----------
  sorted_vec
      Sorted (past) target vector.
  target
      Target that will be transformed.
  slopes
      Slopes of the piece-wise linear function.
  intercepts
      Intercepts of the piece-wise linear function
  
  Returns
  -------
  transformed_target
      Transformed target vector.
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code: def _forward_transform(
      self,
      sorted_vec: np.ndarray,
      target: np.ndarray,
      slopes: np.ndarray,
      intercepts: np.ndarray,
  ) -> np.ndarray:
      """
      Applies the forward transformation to the marginals of the multivariate
      target. Target (real valued) -> empirical cdf [0, 1]
  
      Parameters
      ----------
      sorted_vec
          Sorted (past) target vector.
      target
          Target that will be transformed.
      slopes
          Slopes of the piece-wise linear function.
      intercepts
          Intercepts of the piece-wise linear function
  
      Returns
      -------
      transformed_target
          Transformed target vector.
      """
      transformed = list()
      for sorted_vector, t, slope, intercept in zip(
          sorted_vec.transpose(),
          target.transpose(),
          slopes.transpose(),
          intercepts.transpose(),
      ):
          indices = self._search_sorted(sorted_vector, t)
          transformed_value = slope[indices] * t + intercept[indices]
          transformed.append(transformed_value)
      return np.array(transformed).transpose()
  
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 8 for calling the CDFtoGaussianTransform obj's underlying member method code with example usage: obj._forward_transform(sorted_vec, target, slopes, intercepts) and returns: np