class_docstr: This class generates two samples from the standard normal
  distribution.  These two samples are mixed with n
  mixing coefficients. The coefficients are generated
  by ``np.linspace(0, 1, n_interpolations)``
code: def __init__(
      self,
      names=("vector",),
      vector_size=(100,),
      batch_size=10,
      n_interpolations=10,
      mean=0.0,
      stddev=1.0,
      repeat=1,
      queue_length=10,
      name="random_vector_sampler",
  ):
      # repeat=None for infinite loops
      self.n_interpolations = max(n_interpolations, 1)
      self.mean = mean
      self.stddev = stddev
      self.repeat = repeat
      self.names = names
  
      ImageWindowDataset.__init__(
          self,
          reader=None,
          window_sizes={names[0]: {"spatial_window_size": vector_size}},
          batch_size=batch_size,
          queue_length=queue_length,
          shuffle=False,
          epoch=1,
          smaller_final_batch_mode="drop",
          name=name,
      )
      self.window = ImageWindow(
          shapes={names[0]: vector_size}, dtypes={names[0]: tf.float32}
      )
      tf.logging.info(
          "initialised sampler output %s ", self.window.shapes
      )
  
file_path: NiftyNet/niftynet/engine/sampler_random_vector_v2.py
imports: from __future__ import absolute_import, print_function, division
  import numpy
  import tensorflow
  from niftynet.engine.image_window_dataset import ImageWindowDataset
  from niftynet.engine.image_window import N_SPATIAL, LOCATION_FORMAT, ImageWindow
label_desc: Use image node_id 1 to create a new RandomVectorSampler object from inherited base classes: ImageWindowDataset with example: obj = RandomVectorSampler(names, vector_size, batch_size, n_interpolations, mean, stddev, repeat, queue_length, name)