code: def build_model_with_uniform_prior_no_transforms(
      data: Tuple[AnyNDArray, AnyNDArray],
      prior_on: PriorOn,
      prior_width: float,
  ) -> GPR:
      def parameter(value: tf.Tensor) -> gpflow.Parameter:
          low_value = -100
          high_value = low_value + prior_width
          prior = Uniform(
              low=np.float64(low_value), high=np.float64(high_value)
          )
          return gpflow.Parameter(
              value,
              transform=tfp.bijectors.Identity(),
              prior=prior,
              prior_on=prior_on,
          )
  
      k = gpflow.kernels.Matern52(lengthscales=0.3)
      k.variance = parameter(k.variance)
      k.lengthscales = parameter(k.lengthscales)
  
      mf = gpflow.mean_functions.Linear(1.0, 0.0)
      mf.A = parameter(mf.A)
      mf.b = parameter(mf.b)
      m = GPR(data, k, mf, noise_variance=0.01)
      m.likelihood.variance = parameter(m.likelihood.variance)
      return m
  
imports: from typing import Tuple
  import numpy
  import pytest
  import tensorflow
  import tensorflow_probability
  from check_shapes import ShapeChecker, check_shapes
  from tensorflow_probability.python.distributions import Gamma, Uniform
  import gpflow
  from gpflow.base import AnyNDArray, PriorOn
  from gpflow.models import GPR
  from gpflow.utilities import to_default_float
label_desc: Use image node_id 3 for calling a global function with example usage: build_model_with_uniform_prior_no_transforms(data, prior_on, prior_width) and returns: m, gpflow