class_docstr: computing 1d convolution one each spatial dimension of the input
  using one-dimensional filter.
class_docstr_tok: ["computing","1d","convolution","one","each","spatial","dimension","of","the","input","using","one-dimensional","filter","."]
docstr: :param sigma: standard deviation
  :param truncate: Truncate the filter at this many standard deviations
  :param type_str: type of kernels
docstr_tok: [":","param","sigma",":","standard","deviation",":","param","truncate",":","Truncate","the","filter","at","this","many","standard","deviations",":","param","type_str",":","type","of","kernels"]
code: def __init__(self, sigma=1, truncate=3.0, type_str="gaussian"):
      """
  
      :param sigma: standard deviation
      :param truncate: Truncate the filter at this many standard deviations
      :param type_str: type of kernels
      """
      Layer.__init__(self, name="approximated_smoothing")
      self.kernel_func = look_up_operations(
          type_str.lower(), SUPPORTED_KERNELS
      )
      self.sigma = sigma
      self.truncate = truncate
  
file_path: NiftyNet/niftynet/layer/approximated_smoothing.py
imports: from __future__ import absolute_import, print_function, division
  import tensorflow
  from niftynet.layer.base_layer import Layer
  from niftynet.layer.layer_util import expand_spatial_params, infer_spatial_rank
  from niftynet.utilities.util_common import look_up_operations
label_desc: Use image node_id 1 to create a new SmoothingLayer object from inherited base classes: Layer with example: obj = SmoothingLayer(sigma, truncate, type_str)