text: import numpy as np
  import tensorflow as tf
  from gym.spaces import Discrete, Box, MultiDiscrete
  
  
  def observation_placeholder(ob_space, batch_size=None, name='Ob'):
      '''
      Create placeholder to feed observations into of the size appropriate to the observation space
  
      Parameters:
      ----------
  
      ob_space: gym.Space     observation space
  
      batch_size: int         size of the batch to be fed into input. Can be left None in most cases.
  
      name: str               name of the placeholder
  
      Returns:
      -------
  
      tensorflow placeholder tensor
      '''
  
      assert isinstance(ob_space, Discrete) or isinstance(ob_space, Box) or isinstance(ob_space, MultiDiscrete), \
          'Can only deal with Discrete and Box observation spaces for now'
  
      dtype = ob_space.dtype
      if dtype == np.int8:
          dtype = np.uint8
  
      return tf.placeholder(shape=(batch_size,) + ob_space.shape, dtype=dtype, name=name)
  
  
  def observation_input(ob_space, batch_size=None, name='Ob'):
      '''
      Create placeholder to feed observations into of the size appropriate to the observation space, and add input
      encoder of the appropriate type.
      '''
  
      placeholder = observation_placeholder(ob_space, batch_size, name)
      return placeholder, encode_observation(ob_space, placeholder)
  
  
  def encode_observation(ob_space, placeholder):
      '''
      Encode input in the way that is appropriate to the observation space
  
      Parameters:
      ----------
  
      ob_space: gym.Space             observation space
  
      placeholder: tf.placeholder     observation input placeholder
      '''
      if isinstance(ob_space, Discrete):
          return tf.to_float(tf.one_hot(placeholder, ob_space.n))
      elif isinstance(ob_space, Box):
          return tf.to_float(placeholder)
      elif isinstance(ob_space, MultiDiscrete):
          placeholder = tf.cast(placeholder, tf.int32)
          one_hots = [tf.to_float(tf.one_hot(placeholder[..., i], ob_space.nvec[i])) for i in
                      range(placeholder.shape[-1])]
          return tf.concat(one_hots, axis=-1)
      else:
          raise NotImplementedError
  