old_contents: class Config(object):
      win_size = 8
      bandwidth = win_size**2
      batch_size = 32
      eval_batch_size = 50
      loc_std = 0.22
      # zise of MNIST image, 28px x 28px
      original_size = 28
      # number of channels in the image, MNIST has 1 channel, i.e. white channel
      num_channels = 1
      depth = 1
      sensor_size = win_size**2 * depth
      minRadius = 8
      hg_size = hl_size = 128
      g_size = 256
      cell_output_size = 256
      loc_dim = 2
      cell_size = 256
      cell_out_size = cell_size
      num_glimpses = 6
      num_classes = 10
      max_grad_norm = 5.
  
      # amount of steps for training the model
      step = 100000
      lr_start = 1e-3
      lr_min = 1e-4
  
      # Monte Carlo sampling
      M = 10
  
new_contents: class Config(object):
      win_size = 8
      bandwidth = win_size**2
      batch_size = 32
      eval_batch_size = 50
      # standard deviation for
      # the gaussian distribution
      # when added to location
      loc_std = 0.22
      # zise of MNIST image, 28px x 28px
      original_size = 28
      # number of channels in the image, MNIST has 1 channel, i.e. white channel
      num_channels = 1
      #  number of patches which should be extracted in a glimpse
      n_patches = 1
      sensor_size = win_size**2 * n_patches
      minRadius = 8
      # Size of parameters for Glimpse network
      hg_size = hl_size = 128
      g_size = 256
  
      cell_output_size = 256
      # location shape --> (x, y)
      loc_dim = 2
      cell_size = 256
  
      num_glimpses = 6
      num_classes = 10
      max_grad_norm = 5.
  
      # amount of steps for training the model
      step = 100000
      lr_start = 1e-3
      lr_min = 1e-4
  
      # Monte Carlo sampling
      M = 10
  
subject: clean up and comment configuration object