class_docstr: Poisson distribution, i.e. the distribution of the number of
  successes in a specified region.
  
  Parameters
  ----------
  rate
      Tensor containing the means, of shape `(*batch_shape, *event_shape)`.
  F
class_docstr_tok: ["Poisson","distribution",",","i.e",".","the","distribution","of","the","number","of","successes","in","a","specified","region",".","Parameters","--","--","--","--","--","rate","Tensor","containing","the","means",",","of","shape","`","(","*","batch_shape",",","*","event_shape",")","`",".","F"]
code: def sample(
      self, num_samples: Optional[int] = None, dtype=np.float32
  ) -> Tensor:
      def s(rate: Tensor) -> Tensor:
          return self.F.random.poisson(lam=rate, dtype=dtype)
  
      return _sample_multiple(
          s, rate=self.rate, num_samples=num_samples
      )
  
imports: from typing import Dict, List, Optional, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .deterministic import DeterministicOutput
  from .distribution import Distribution, _sample_multiple, getF, softplus
  from .distribution_output import DistributionOutput
  from .mixture import MixtureDistributionOutput
label_desc: Use image node_id 9 for calling the Poisson obj's underlying member method code with example usage: obj.sample(num_samples, dtype) and returns: _sample_multiple, self