class_docstr: A recommendation environment which generates items with visible features
  randomly (parametric actions).
  The environment can be configured to be multi-user, i.e. different models
  will be learned independently for each user, by setting num_users_in_db
  parameter.
  To enable slate recommendation, the `slate_size` config parameter can be
  set as > 1.
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code: def _get_embedding(self):
      return np.random.uniform(
          -1, 1, size=(self.embedding_size,)
      ).astype(np.float32)
  
file_path: ray/python/ray/rllib/examples/env/bandit_envs_recommender_system.py
imports: import gymnasium
  import numpy
  from typing import Optional
  from ray.rllib.utils.numpy import softmax
label_desc: Use image node_id 2 for calling the ParametricRecSys obj's underlying member method code with example usage: obj._get_embedding() and returns: np