class_docstr: Defines a Gluon block used for GP training and predictions.
class_docstr_tok: ["Defines","a","Gluon","block","used","for","GP","training","and","predictions","."]
docstr: Parameters
  ----------
  prediction_length
      Prediction length.
  context_length
      Training length.
  cardinality
      Number of time series.
  kernel_output
      KernelOutput instance to determine which kernel subclass to be
      instantiated.
  params_scaling
      Determines whether or not to scale the model parameters.
  float_type
      Determines whether to use single or double precision.
  max_iter_jitter
      Maximum number of iterations for jitter to iteratively make the
      matrix positive definite.
  jitter_method
      Iteratively jitter method or use eigenvalue decomposition depending
      on problem size.
  **kwargs
      Arbitrary keyword arguments.
docstr_tok: ["Parameters","--","--","--","--","--","prediction_length","Prediction","length",".","context_length","Training","length",".","cardinality","Number","of","time","series",".","kernel_output","KernelOutput","instance","to","determine","which","kernel","subclass","to","be","instantiated",".","params_scaling","Determines","whether","or","not","to","scale","the","model","parameters",".","float_type","Determines","whether","to","use","single","or","double","precision",".","max_iter_jitter","Maximum","number","of","iterations","for","jitter","to","iteratively","make","the","matrix","positive","definite",".","jitter_method","Iteratively","jitter","method","or","use","eigenvalue","decomposition","depending","on","problem","size",".","*","*","kwargs","Arbitrary","keyword","arguments","."]
code: def __init__(
      self,
      prediction_length: int,
      context_length: int,
      cardinality: int,
      kernel_output: KernelOutputDict,
      params_scaling: bool,
      float_type: Type,
      max_iter_jitter: int,
      jitter_method: str,
      **kwargs,
  ) -> None:
      """
      Parameters
      ----------
      prediction_length
          Prediction length.
      context_length
          Training length.
      cardinality
          Number of time series.
      kernel_output
          KernelOutput instance to determine which kernel subclass to be
          instantiated.
      params_scaling
          Determines whether or not to scale the model parameters.
      float_type
          Determines whether to use single or double precision.
      max_iter_jitter
          Maximum number of iterations for jitter to iteratively make the
          matrix positive definite.
      jitter_method
          Iteratively jitter method or use eigenvalue decomposition depending
          on problem size.
      **kwargs
          Arbitrary keyword arguments.
      """
      super().__init__(**kwargs)
  
      self.prediction_length = prediction_length
      self.context_length = context_length
      self.cardinality = cardinality
      self.kernel_output = kernel_output
      self.params_scaling = params_scaling
      self.float_type = float_type
      self.max_iter_jitter = max_iter_jitter
      self.jitter_method = jitter_method
  
      with self.name_scope():
          self.proj_kernel_args = kernel_output.get_args_proj(
              self.float_type
          )
          self.num_hyperparams = kernel_output.get_num_args()
          self.embedding = mx.gluon.nn.Embedding(
              # Noise sigma is additional parameter so add 1 to output dim
              input_dim=self.cardinality,
              output_dim=self.num_hyperparams + 1,
              dtype=self.float_type,
          )
  
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 GaussianProcessNetworkBase object from inherited base classes: mx.gluon with example: obj = GaussianProcessNetworkBase(prediction_length, context_length, cardinality, kernel_output, params_scaling, float_type, max_iter_jitter, jitter_method)