code: def main():
      # Initialize problem parameters
      batch_size = 1
      prediction_length = 50
      context_length = 5
      axis = [-5, 5, -3, 3]
      float_type = np.float64
  
      num_samples = 3
      ts_idx = 0
  
      # Initialize test data to generate Gaussian Process from
      lb = -5
      ub = 5
      dx = (ub - lb) / (prediction_length - 1)
      x_test = nd.arange(lb, ub + dx, dx, dtype=float_type).reshape(
          -1, 1
      )
      x_test = nd.tile(x_test, reps=(batch_size, 1, 1))
  
      # Define the GP hyper parameters
      amplitude = nd.ones((batch_size, 1, 1), dtype=float_type)
      length_scale = math.sqrt(0.4) * nd.ones_like(amplitude)
      sigma = math.sqrt(1e-5) * nd.ones_like(amplitude)
  
      # Instantiate desired kernel object and compute kernel matrix
      rbf_kernel = RBFKernel(amplitude, length_scale)
  
      # Generate samples from 0 mean Gaussian process with RBF Kernel and plot it
      gp = GaussianProcess(
          sigma=sigma,
          kernel=rbf_kernel,
          prediction_length=prediction_length,
          context_length=context_length,
          num_samples=num_samples,
          float_type=float_type,
          sample_noise=False,  # Returns sample without noise
      )
      mean = nd.zeros((batch_size, prediction_length), dtype=float_type)
      covariance = rbf_kernel.kernel_matrix(x_test, x_test)
      gp.plot(
          x_test=x_test,
          samples=gp.sample(mean, covariance),
          ts_idx=ts_idx,
      )
  
      # Generate training set on subset of interval using the sine function
      x_train = nd.array([-4, -3, -2, -1, 1], dtype=float_type).reshape(
          context_length, 1
      )
      x_train = nd.tile(x_train, reps=(batch_size, 1, 1))
      y_train = nd.sin(x_train.squeeze(axis=2))
  
      # Predict exact GP using the GP predictive mean and covariance using the same fixed hyper-parameters
      samples, predictive_mean, predictive_std = gp.exact_inference(
          x_train, y_train, x_test
      )
  
      assert (
          np.sum(np.isnan(samples.asnumpy())) == 0
      ), "NaNs in predictive samples!"
  
      gp.plot(
          x_train=x_train,
          y_train=y_train,
          x_test=x_test,
          ts_idx=ts_idx,
          mean=predictive_mean,
          std=predictive_std,
          samples=samples,
          axis=axis,
      )
  
imports: import math
  from gluonts.mx.kernels import RBFKernel
  from gluonts.mx.model.gp_forecaster.gaussian_process import GaussianProcess
  import mxnet.ndarray
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types