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: Constructor for CDFtoGaussianTransform.
  
  Parameters
  ----------
  target_dim
      Dimensionality of the target.
  target_field
      Field that will be transformed.
  observed_values_field
      Field that indicates observed values.
  cdf_suffix
      Suffix to mark the field with the transformed target.
  max_context_length
      Sets the maximum context length for the empirical CDF.
  dtype
      numpy dtype of output.
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code: def __init__(
      self,
      target_dim: int,
      target_field: str,
      observed_values_field: str,
      cdf_suffix="_cdf",
      max_context_length: Optional[int] = None,
      dtype: Type = np.float32,
  ) -> None:
      """
      Constructor for CDFtoGaussianTransform.
  
      Parameters
      ----------
      target_dim
          Dimensionality of the target.
      target_field
          Field that will be transformed.
      observed_values_field
          Field that indicates observed values.
      cdf_suffix
          Suffix to mark the field with the transformed target.
      max_context_length
          Sets the maximum context length for the empirical CDF.
      dtype
          numpy dtype of output.
      """
      self.target_field = target_field
      self.past_target_field = "past_" + self.target_field
      self.future_target_field = "future_" + self.target_field
      self.past_observed_field = f"past_{observed_values_field}"
      self.sort_target_field = f"past_{target_field}_sorted"
      self.slopes_field = "slopes"
      self.intercepts_field = "intercepts"
      self.cdf_suffix = cdf_suffix
      self.max_context_length = max_context_length
      self.target_dim = target_dim
      self.dtype = dtype
  
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 1 to create a new CDFtoGaussianTransform object from inherited base classes: MapTransformation with example: obj = CDFtoGaussianTransform(target_dim, target_field, observed_values_field, cdf_suffix, max_context_length, dtype)