text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.optim as optim
  import numpy as np
  """
      One way to improve or interprete the former function is that:
          the network learns a displacement towards the bottom of the feature map -> sample features there
  """
  
  class LookGround(nn.Module):
      """ Some Information about LookGround (GAC) """
      def __init__(self, input_features, baseline=0.54, relative_elevation=1.65):
          
          # parent Class __init__ method
          super(LookGround, self).__init__()
          
          # disparity create
          self.disp_create = nn.Sequential(
              nn.Conv2d(input_features, 1, 3, padding=1),
              nn.Tanh(),
          )
          
          # Conv2d Layer
          self.extract = nn.Conv2d(1 + input_features, input_features, 1)
          
          # virtual stereo setup baseline B = 0.54m
          self.baseline = baseline
          
          # expected elevation of camera from the ground (1.65 meters)
          self.relative_elevation = relative_elevation
          
          # observation angle
          self.alpha = nn.Parameter(torch.tensor([0.0], dtype=torch.float32))
  
      def forward(self, inputs):
          # input feature map
          x = inputs['features']
          P2 = inputs['P2']
           
          # downsample to 16.0
          P2 = P2.clone()
          P2[:, 0:2] /= 16.0 
          
          disp = self.disp_create(x)
          disp = 0.1 * (0.05 * disp + 0.95 * disp.detach())
  
          batch_size, _, height, width = x.size()
  
          # create Disparity Map
          h, w = x.shape[2], x.shape[3]
          x_range = np.arange(h, dtype=np.float32)
          y_range = np.arange(w, dtype=np.float32)
          _, yy_grid  = np.meshgrid(y_range, x_range)
  
          yy_grid =  x.new(yy_grid).unsqueeze(0) #[1, H, W]
          fy =  P2[:, 1:2, 1:2] #[B, 1, 1]
          cy =  P2[:, 1:2, 2:3] #[B, 1, 1]
          Ty =  P2[:, 1:2, 3:4] #[B, 1, 1]
          
          # compute disparity
          disparity = fy * self.baseline  * (yy_grid - cy) / (torch.abs(fy * self.relative_elevation + Ty) + 1e-10)
          disparity = F.relu(disparity)
  
          # original coordinates of pixels
          x_base = torch.linspace(-1, 1, width).repeat(batch_size,
                      height, 1).type_as(x)
          y_base = torch.linspace(-1, 1, height).repeat(batch_size,
                      width, 1).transpose(1, 2).type_as(x)
  
          # apply shift in Y direction
          h_mean = 1.535
          y_shifts_base = F.relu(
              h_mean * (yy_grid - cy) / (2 * (self.relative_elevation - 0.5 * h_mean))
          ) / (yy_grid.shape[1] * 0.5) # [1, H, W]
          y_shifts = y_shifts_base + disp[:, 0, :, :]  # Disparity is passed in NCHW format with 1 channel
          flow_field = torch.stack((x_base, y_base + y_shifts), dim=3)
  
  
          # In grid_sample coordinates are assumed to be between -1 and 1
          features = torch.cat([disparity.unsqueeze(1), x], dim=1)
          output = F.grid_sample(features, flow_field, mode='bilinear',
                                 padding_mode='border', align_corners=True)
  
          return F.relu(x + self.extract(output) * self.alpha)
  