code: def log_softmax(x, axis=None):
      max_x = np.max(x, axis=axis, keepdims=True)
      logsumexp = np.log(
          np.exp(x - max_x).sum(axis=axis, keepdims=True)
      )
      return x - max_x - logsumexp
  
imports: import jax
  import numpy
  from jax import lax
  from jax import numpy
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_jax
  from keras.backend.config import epsilon
  from keras.backend.numpy.core import cast
  from keras.backend.numpy.core import convert_to_tensor
  from keras.backend.numpy.core import is_tensor
  from keras.utils.module_utils import scipy
label_desc: Use image node_id 16 for calling a global function with example usage: log_softmax(x, axis) and returns: unknown