code: def test_softmax_skipped(backend, io_type):
      X = np.random.rand(100, 10)
      dense = tf.keras.layers.Dense(14, input_shape=(10,), name="dense")
      softmax = tf.keras.layers.Activation(
          activation="softmax", name="softmax"
      )
      model = tf.keras.models.Sequential([dense, softmax])
      model.compile()
  
      cfg = hls4ml.utils.config_from_keras_model(
          model, granularity="name"
      )
      cfg["LayerName"]["softmax"]["skip"] = True
  
      odir = str(
          test_root_path / "hls4mlprj_softmax_skipped_{}_{}"
      ).format(backend, io_type)
      hls_model = hls4ml.converters.convert_from_keras_model(
          model,
          hls_config=cfg,
          io_type=io_type,
          output_dir=odir,
          backend=backend,
      )
      hls_model.compile()
  
      # Verify Softmax was removed
      hls_layers = list(
          hls_model.get_layers()
      )  # 0 is Input, 1 is Dense, 2 is Softmax (if not removed)
      assert len(hls_layers) == 2
  
      # Verify hls4ml output is equal to Dense output
      y_keras_dense = dense(X).numpy()  # type: ignore
      y_hls4ml = hls_model.predict(X).reshape(y_keras_dense.shape)  # type: ignore
      np.testing.assert_allclose(
          y_hls4ml, y_keras_dense, rtol=0, atol=2e-2
      )
  
file_path: hls4ml/test/pytest/test_softmax.py
imports: from pathlib import Path
  import numpy
  import pytest
  import tensorflow
  from sklearn.metrics import accuracy_score
  import hls4ml
label_desc: Use image node_id 3 for calling a global function with example usage: test_softmax_skipped(backend, io_type) without return types