class_docstr: Computes a covariance matrix based on the RBF (squared exponential) kernel
  between inputs :math:`\mathbf{x_1}` and :math:`\mathbf{x_2}`:
  :math:`k_{\text{RBF}}(\mathbf{x_1}, \mathbf{x_2}) = \theta_0 \exp \left
  ( -\frac{\|\mathbf{x_1} - \mathbf{x_2}\|^2}
  {2\theta_1^2} \right)`,
  where :math:`\theta_0` is the amplitude parameter and
  :math:`\theta_1` is the length scale parameter.
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docstr: Parameters
  ----------
  amplitude : Tensor
      RBF kernel amplitude hyper-parameter of shape (batch_size, 1, 1).
  length_scale : Tensor
      RBF kernel length scale hyper-parameter of of shape
      (batch_size, 1, 1).
  F : ModuleType
      A module that can either refer to the Symbol API or the NDArray
      API in MXNet.
docstr_tok: ["Parameters","--","--","--","--","--","amplitude",":","Tensor","RBF","kernel","amplitude","hyper-parameter","of","shape","(","batch_size",",","1",",","1",")",".","length_scale",":","Tensor","RBF","kernel","length","scale","hyper-parameter","of","of","shape","(","batch_size",",","1",",","1",")",".","F",":","ModuleType","A","module","that","can","either","refer","to","the","Symbol","API","or","the","NDArray","API","in","MXNet","."]
code: def __init__(
      self, amplitude: Tensor, length_scale: Tensor, F=None
  ) -> None:
      """
      Parameters
      ----------
      amplitude : Tensor
          RBF kernel amplitude hyper-parameter of shape (batch_size, 1, 1).
      length_scale : Tensor
          RBF kernel length scale hyper-parameter of of shape
          (batch_size, 1, 1).
      F : ModuleType
          A module that can either refer to the Symbol API or the NDArray
          API in MXNet.
      """
      self.F = F if F else getF(amplitude)
      self.amplitude = amplitude
      self.length_scale = length_scale
  
file_path: gluonts/src/gluonts/mx/kernels/_rbf_kernel.py
imports: import math
  from typing import Dict, Tuple
  from gluonts.mx import Tensor
  from gluonts.mx.distribution.distribution import getF, softplus
  from .None import Kernel, KernelOutputDict
label_desc: Use image node_id 1 to create a new RBFKernel object from inherited base classes: Kernel with example: obj = RBFKernel(amplitude, length_scale, F)