class_docstr: Noise from BART.
class_docstr_tok: ["Noise","from","BART","."]
code: def _make_poisson(self, poisson_lambda):
      lambda_to_the_k = 1
      e_to_the_minus_lambda = math.exp(-poisson_lambda)
      k_factorial = 1
      ps = []
      for k in range(0, 128):
          ps.append(
              e_to_the_minus_lambda * lambda_to_the_k / k_factorial
          )
          lambda_to_the_k *= poisson_lambda
          k_factorial *= k + 1
          if ps[-1] < 0.0000001:
              break
      ps = torch.FloatTensor(ps)
      return torch.distributions.Categorical(ps)
  
imports: import math
  import numpy
  import torch
  from typing import Sequence, Callable
  from onmt.constants import DefaultTokens, SubwordMarker
  from onmt.transforms import register_transform
  from .transform import Transform
label_desc: Use image node_id 3 for calling the BARTNoising obj's underlying member method code with example usage: obj._make_poisson(poisson_lambda) and returns: torch