code: def hard_sigmoid(x):
      # python numbers will be promoted to float64 by np, so it's neccessary to
      # first convert the python numbers to np scalars
      x = x / np.array(6.0, x.dtype) + np.array(0.5, x.dtype)
      return np.where(
          x <= 0.0,
          np.array(0.0, x.dtype),
          np.where(x >= 1.0, np.array(1.0, x.dtype), x),
      )
  
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 10 for calling a global function with example usage: hard_sigmoid(x) and returns: np