code: def test_embedder(reset):
      embedder = VectorEmbedder(
          np.array([10, 10]), name="test", scheme=EmbedderScheme.Empty
      )
  
      # make sure the ops where not created yet
      assert len(tf.get_default_graph().get_operations()) == 0
  
      # call the embedder
      input_ph, output_ph = embedder()
  
      # make sure that now the ops were created
      assert len(tf.get_default_graph().get_operations()) > 0
  
      # try feeding a batch of one example  # TODO: consider auto converting to batch
      input = np.random.rand(1, 10, 10)
      sess = tf.Session()
      output = sess.run(embedder.output, {embedder.input: input})
      assert output.shape == (1, 100)  # should have flattened the input
  
      # now make sure the returned placeholders behave the same
      output = sess.run(output_ph, {input_ph: input})
      assert output.shape == (1, 100)  # should have flattened the input
  
      # make sure the naming is correct
      assert embedder.get_name() == "test"
  
file_path: coach/rl_coach/tests/architectures/tensorflow_components/embedders/test_identity_embedder.py
imports: import os
  import sys
  from rl_coach.base_parameters import EmbedderScheme
  import pytest
  import numpy
  from rl_coach.architectures.tensorflow_components.embedders.vector_embedder import VectorEmbedder
  import tensorflow
  from tensorflow import logging
label_desc: Use image node_id 2 for calling a global function with example usage: test_embedder(reset) without return types