code: def _generate_observations(
      self, batch_size: int, accepts_per_arm_features: bool
  ) -> types.NestedTensor:
      if accepts_per_arm_features:
          num_actions = self._num_actions(accepts_per_arm_features)
          observations = {
              bandit_spec_utils.GLOBAL_FEATURE_KEY: np.array(
                  np.random.normal(size=[batch_size, 3]),
                  dtype=np.float32,
              ),
              bandit_spec_utils.PER_ARM_FEATURE_KEY: np.array(
                  np.random.normal(size=[batch_size, num_actions, 3]),
                  dtype=np.float32,
              ),
              bandit_spec_utils.NUM_ACTIONS_FEATURE_KEY: num_actions
              * tf.ones([batch_size], dtype=tf.int32),
          }
      else:
          observations = np.array(
              np.random.normal(size=[batch_size, 3]), dtype=np.float32
          )
      return observations
  
imports: from typing import Optional
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.agents import neural_falcon_agent
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
label_desc: Use image node_id 5 for calling the NeuralFalconAgentTest obj's underlying member method code with example usage: obj._generate_observations(batch_size, accepts_per_arm_features) and returns: observations