class_docstr: Class to connect a network to a distribution that is transformed
  by a sequence of learnable bijections.
class_docstr_tok: ["Class","to","connect","a","network","to","a","distribution","that","is","transformed","by","a","sequence","of","learnable","bijections","."]
code: def _split_args(self, args):
      # Since hybrid_forward does not support dictionary, we have to separate
      # the raw outputs of the network based on the indices and map them to
      # the learnable parameters
      num_distr_args = len(self.base_distr_args_dim)
      distr_args = args[0:num_distr_args]
  
      num_transforms_args = [
          len(transform_dim_args)
          for transform_dim_args in self.transforms_args_dim
      ]
      # starting indices of arguments for each transformation
      num_args_cumsum = np.cumsum(
          [num_distr_args] + num_transforms_args
      )
  
      # get the arguments for each of the transformations
      transforms_args = list(
          map(
              lambda ixs: args[ixs[0] : ixs[1]],
              zip(num_args_cumsum, num_args_cumsum[1:]),
          )
      )
  
      return distr_args, transforms_args
  
file_path: gluonts/src/gluonts/mx/distribution/transformed_distribution_output.py
imports: from collections import ChainMap
  from typing import List, Optional, Tuple
  import numpy
  from mxnet import gluon
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .None import Distribution
  from .bijection import AffineTransformation
  from .bijection_output import BijectionOutput
  from .distribution_output import ArgProj, DistributionOutput
  from .transformed_distribution import TransformedDistribution
label_desc: Use image node_id 3 for calling the TransformedDistributionOutput obj's underlying member method code with example usage: obj._split_args(args) and returns: distr_args,  transforms_args