class_docstr: Class to connect a network to a kernel.
class_docstr_tok: ["Class","to","connect","a","network","to","a","kernel","."]
docstr: This function computes the standard deviation of the data along a given
  axis.
  
  Parameters
  ----------
  F : ModuleType
      A module that can either refer to the Symbol API or the NDArray
      API in MXNet.
  data : Tensor
      Data to be used to compute the standard deviation.
  axis : int
      Axis along which to compute the standard deviation.
  
  Returns
  -------
  Tensor
      The standard deviation of the given data.
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code: def compute_std(F, data: Tensor, axis: int) -> Tensor:
      """
      This function computes the standard deviation of the data along a given
      axis.
  
      Parameters
      ----------
      F : ModuleType
          A module that can either refer to the Symbol API or the NDArray
          API in MXNet.
      data : Tensor
          Data to be used to compute the standard deviation.
      axis : int
          Axis along which to compute the standard deviation.
  
      Returns
      -------
      Tensor
          The standard deviation of the given data.
      """
      return F.sqrt(
          F.mean(
              F.broadcast_minus(
                  data, F.mean(data, axis=axis).expand_dims(axis=axis)
              )
              ** 2,
              axis=axis,
          )
      )
  
file_path: gluonts/src/gluonts/mx/kernels/_kernel_output.py
imports: from typing import Dict, Type
  import numpy
  from mxnet import gluon
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.distribution.distribution_output import ArgProj
  from .None import Kernel
label_desc: Use image node_id 3 for calling the KernelOutput obj's underlying member method code with example usage: obj.compute_std(F, data, axis) and returns: F