class_docstr: Create a Poisson distribution object.
  
  Parameters
  ----------
  rate : Tensor or scalar, default 1
      rate parameter of the distribution.
code: def sample_n(self, size=None):
      lam = self.rate
      if isinstance(lam, Number):
          # Scalar case
          if size is None:
              size = ()
          return npx.scalar_poisson(lam, size)
      else:
          return np.moveaxis(npx.tensor_poisson(lam, size), -1, 0)
  
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 6 for calling the Poisson obj's underlying member method code with example usage: obj.sample_n(size) and returns: npx, np