class_docstr: Reparameterizer to split a random variable along a dimension, similar to
  :func:`torch.split`.
  
  This is useful for treating different parts of a tensor with different
  reparameterizers or inference methods. For example when performing HMC
  inference on a time series, you can first apply
  :class:`~pyro.infer.reparam.discrete_cosine.DiscreteCosineReparam` or
  :class:`~pyro.infer.reparam.haar.HaarReparam`, then apply
  :class:`SplitReparam` to split into low-frequency and high-frequency
  components, and finally add the low-frequency components to the
  ``full_mass`` matrix together with globals.
  
  :param sections: Size of a single chunk or list of sizes for
      each chunk.
  :type: list(int)
  :param int dim: Dimension along which to split. Defaults to -1.
code: def __init__(self, sections, dim):
      assert isinstance(dim, int) and dim < 0
      assert isinstance(sections, list)
      assert all(isinstance(size, int) for size in sections)
      self.event_dim = -dim
      self.sections = sections
  
file_path: pyro/pyro/infer/reparam/split.py
imports: import torch
  import pyro
  import pyro.distributions
  import pyro.poutine
  from .reparam import Reparam
label_desc: Use image node_id 1 to create a new SplitReparam object from inherited base classes: Reparam with example: obj = SplitReparam(sections, dim)