code: def multi_hot(x, num_classes, axis=-1, dtype="float32"):
      x = convert_to_tensor(x)
      reduction_axis = 1 if len(x.shape) > 1 else 0
      outputs = np.max(
          one_hot(
              cast(x, "int32"), num_classes, axis=axis, dtype=dtype
          ),
          axis=reduction_axis,
      )
      return outputs
  
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 27 for calling a global function with example usage: multi_hot(x, num_classes, axis, dtype) and returns: outputs