code: def lmer_model(
      fixed_effects_sd,
      n_groups,
      random_effects_alpha,
      random_effects_beta,
      fixed_effects_label="w",
      random_effects_label="u",
      observation_label="y",
      response="normal",
  ):
      return partial(
          bayesian_linear_model,
          w_means={fixed_effects_label: torch.tensor(0.0)},
          w_sqrtlambdas={fixed_effects_label: 1.0 / fixed_effects_sd},
          obs_sd=torch.tensor(1.0),
          re_group_sizes={random_effects_label: n_groups},
          re_alphas={random_effects_label: random_effects_alpha},
          re_betas={random_effects_label: random_effects_beta},
          response=response,
          response_label=observation_label,
      )
  
imports: import warnings
  from collections import OrderedDict
  from contextlib import ExitStack
  from functools import partial
  import torch
  from torch.distributions import constraints
  from torch.distributions.transforms import AffineTransform, SigmoidTransform
  from torch.nn.functional import softplus
  import pyro
  import pyro.distributions
  from pyro.contrib.util import iter_plates_to_shape, rmv
label_desc: Use image node_id 9 for calling a global function with example usage: lmer_model(fixed_effects_sd, n_groups, random_effects_alpha, random_effects_beta, fixed_effects_label, random_effects_label, observation_label, response) and returns: partial