class_docstr: Reinterprets some collection of independent, non-identical distributions as
  a single multivariate random variable (convert some `batch_dim` to `event_dim`).
code: def __init__(
      self,
      base_distribution,
      reinterpreted_batch_ndims,
      validate_args=None,
  ):
      event_dim = (
          reinterpreted_batch_ndims + base_distribution.event_dim
      )
      self.base_dist = base_distribution
      self.reinterpreted_batch_ndims = reinterpreted_batch_ndims
      super(Independent, self).__init__(
          event_dim=event_dim, validate_args=validate_args
      )
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/independent.py
imports: from .distribution import Distribution
  from .constraint import dependent_property
  from .utils import sum_right_most
label_desc: Use image node_id 1 to create a new Independent object from inherited base classes: Distribution with example: obj = Independent(base_distribution, reinterpreted_batch_ndims, validate_args)