code: def test_inference(
      gp_params, mean_exact, std_exact, x_full, y_train
  ) -> None:
      # Initialize problem parameters
      tol = 1e-2
      num_samples = 100
      context_length = y_train.shape[1]
      prediction_length = mean_exact.shape[1]
  
      # Extract training and test set
      x_train = x_full[:, :context_length, :]
      x_test = x_full[
          :, context_length : context_length + prediction_length, :
      ]
  
      amplitude = gp_params[:, 0, :].expand_dims(axis=2)
      length_scale = gp_params[:, 1, :].expand_dims(axis=2)
      sigma = gp_params[:, 2, :].expand_dims(axis=2)
  
      # Instantiate RBFKernel with its hyper-parameters
      kernel = RBFKernel(amplitude, length_scale)
  
      # Instantiate gp_inference object and hybridize
      gp = GaussianProcess(
          sigma=sigma,
          kernel=kernel,
          context_length=context_length,
          prediction_length=prediction_length,
          num_samples=num_samples,
          float_type=np.float32,
      )
  
      # Compute predictive mean and covariance
      _, mean, std = gp.exact_inference(x_train, y_train, x_test)
  
      # This test compares to the predictive mean and std generated from MATLAB's fitrgp, which
      # outputs the sample with the noise, i.e. adds :math:`sigma^2` to the diagonal of
      # the predictive covariance matrix.
      assert relative_error(mean, mean_exact) <= tol
      assert relative_error(std, std_exact) <= tol
  
imports: import numpy
  import pytest
  from mxnet import nd
  from gluonts.mx.model.gp_forecaster.gaussian_process import GaussianProcess
  from gluonts.mx.kernels import RBFKernel
  from .data import load_exact_mean, load_exact_std, load_gp_params, load_xfull, load_ytrain
label_desc: Use image node_id 2 for calling a global function with example usage: test_inference(gp_params, mean_exact, std_exact, x_full, y_train) without return types