docstr: Parameters
  ----------
  num_parallel_samples
      Number of samples to be drawn.
  sample_noise
      Boolean to determine whether to add :math:`\sigma^2I` to the
      predictive covariance matrix.
  *args
      Variable length argument list.
  **kwargs
      Arbitrary keyword arguments.
docstr_tok: ["Parameters","--","--","--","--","--","num_parallel_samples","Number","of","samples","to","be","drawn",".","sample_noise","Boolean","to","determine","whether","to","add",":","math",":","`","\\sigma^2I","`","to","the","predictive","covariance","matrix",".","*","args","Variable","length","argument","list",".","*","*","kwargs","Arbitrary","keyword","arguments","."]
code: def __init__(
      self,
      num_parallel_samples: int,
      sample_noise: bool,
      *args,
      **kwargs,
  ) -> None:
      r"""
      Parameters
      ----------
      num_parallel_samples
          Number of samples to be drawn.
      sample_noise
          Boolean to determine whether to add :math:`\sigma^2I` to the
          predictive covariance matrix.
      *args
          Variable length argument list.
      **kwargs
          Arbitrary keyword arguments.
      """
      super().__init__(*args, **kwargs)
      self.num_parallel_samples = num_parallel_samples
      self.sample_noise = sample_noise
  
file_path: gluonts/src/gluonts/mx/model/gp_forecaster/_network.py
imports: from typing import Tuple, Type
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.distribution.distribution import softplus
  from gluonts.mx.kernels import KernelOutputDict
  from .gaussian_process import GaussianProcess
label_desc: Use image node_id 1 to create a new GaussianProcessPredictionNetwork object from inherited base classes: GaussianProcessNetworkBase with example: obj = GaussianProcessPredictionNetwork(num_parallel_samples, sample_noise)