class_docstr: Create a Poisson distribution object.
  
  Parameters
  ----------
  rate : Tensor or scalar, default 1
      rate parameter of the distribution.
code: def broadcast_to(self, batch_shape):
      new_instance = self.__new__(type(self))
      new_instance.rate = np.broadcast_to(self.rate, batch_shape)
      super(Poisson, 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/poisson.py
imports: from numbers import Number
  from .exp_family import ExponentialFamily
  from .constraint import Positive, NonNegativeInteger
  from .utils import gammaln
  from ....None import np, npx
label_desc: Use image node_id 4 for calling the Poisson obj's underlying member method code with example usage: obj.broadcast_to(batch_shape) and returns: new_instance