code: def transform_backend_eta(eta, kwargs):
      data = kwargs["observed"]
  
      # Add column of zeros to the linear predictor for the reference level (the first one)
      shape = (data.shape[0], 1)
  
      # The first line makes sure the intercept-only models work
      eta = (
          np.ones(shape) * eta
      )  # (response_levels, ) -> (n, response_levels)
      eta = pt.concatenate([np.zeros(shape), eta], axis=1)
      return eta
  
imports: import numpy
  import pytensor.tensor
  import xarray
  from bambi.families.family import Family
  from bambi.transformations import transformations_namespace
  from bambi.utils import extract_argument_names, get_aliased_name
label_desc: Use image node_id 7 for calling the Multinomial obj's underlying member method code with example usage: obj.transform_backend_eta(eta, kwargs) and returns: eta