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 broadcast_to(self, batch_shape):
      new_instance = self.__new__(type(self))
      # we use -2 to copy the sizes of reinterpreted batch dimensions
      reinterpreted_axes = (-2,) * self.reinterpreted_batch_ndims
      new_instance.base_dist = self.base_dist.broadcast_to(
          batch_shape + reinterpreted_axes
      )
      new_instance.reinterpreted_batch_ndims = (
          self.reinterpreted_batch_ndims
      )
      super(Independent, new_instance).__init__(
          event_dim=self.event_dim, validate_args=False
      )
      new_instance._validate_args = self._validate_args
      return new_instance
  
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 2 for calling the Independent obj's underlying member method code with example usage: obj.broadcast_to(batch_shape) and returns: new_instance