text: 
  import numpy as np__
  import tensorflow as tf__
  import tensorflow_probability as tfp__
  tfd__ = tfp__.distributions
  tfb__ = tfp__.bijectors
  from tensorflow.python.ops.parallel_for import pfor as pfor__
  
  def yield__(b):
    return b * tf__.cast(3, tf__.float64)
     
  def func(await__):
    return await__ + tf__.cast(1, tf__.float64)
     
  class python_kwrds_model(tfd__.Distribution):
  
    def __init__(self, lambda__):
      self.lambda__ = lambda__
      self.d = lambda__ / tf__.cast(3, tf__.float64)
       
    
    def log_prob_one_chain(self, params):
      target = 0
      
      # Data
      lambda__ = self.lambda__
      
      # Transformed data
      d = self.d
      
      # Parameters
      finally__ = tf__.cast(params[0], tf__.float64)
      
      # Target log probability computation
      assert__ = finally__ + tf__.cast(2, tf__.float64)
      target += tf__.reduce_sum(tfd__.Normal(yield__(assert__),
                                             tf__.cast(1, tf__.float64)).log_prob(d))
      target += tf__.reduce_sum(tfd__.Binomial(tf__.cast(10, tf__.float64),
                                               func(finally__)).log_prob(lambda__))
      return target
       
    def log_prob(self, params):
      return tf__.vectorized_map(self.log_prob_one_chain, params)
      
       
    def parameter_shapes(self, nchains__):
      lambda__ = self.lambda__
      return [(nchains__, )]
       
    def parameter_bijectors(self):
      lambda__ = self.lambda__
      return [tfb__.Identity()]
       
    def parameter_names(self):
      return ["finally__"]
       
  model = python_kwrds_model