class_docstr: generate randomised bias field transformation for data augmentation
class_docstr_tok: ["generate","randomised","bias","field","transformation","for","data","augmentation"]
docstr: Create the bias field map based on the randomly sampled coefficients
  and apply it (multiplicative) to the image to augment
  :param image: image on which to apply the bias field augmentation
  :return: modified image
docstr_tok: ["Create","the","bias","field","map","based","on","the","randomly","sampled","coefficients","and","apply","it","(","multiplicative",")","to","the","image","to","augment",":","param","image",":","image","on","which","to","apply","the","bias","field","augmentation",":","return",":","modified","image"]
code: def _apply_transformation(self, image):
      """
      Create the bias field map based on the randomly sampled coefficients
      and apply it (multiplicative) to the image to augment
      :param image: image on which to apply the bias field augmentation
      :return: modified image
      """
      assert self._bf_coeffs is not None
      bf_map = self._generate_bias_field_map(image.shape)
      bf_image = image * bf_map
      return bf_image
  
file_path: NiftyNet/niftynet/layer/rand_bias_field.py
imports: from __future__ import absolute_import, print_function
  import numpy
  from niftynet.layer.base_layer import RandomisedLayer
label_desc: Use image node_id 7 for calling the RandomBiasFieldLayer obj's underlying member method code with example usage: obj._apply_transformation(image) and returns: bf_image