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 = torch.amax(
          one_hot(
              cast(x, "int32"), num_classes, axis=axis, dtype=dtype
          ),
          dim=reduction_axis,
      )
      return outputs
  
imports: import torch
  import torch.nn.functional
  import tree
  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_torch
  from keras.backend.config import epsilon
  from keras.backend.torch.core import cast
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.numpy import expand_dims
  from keras.backend.torch.numpy import maximum
  from keras.backend.torch.numpy import where
  from keras.utils.argument_validation import standardize_tuple
label_desc: Use image node_id 29 for calling a global function with example usage: multi_hot(x, num_classes, axis, dtype) and returns: outputs