class_docstr: BottleNeck building block. Default: No use. Each block consists of two 1*n*n and one n*n*n convolutional layers
  with a ReLU activation function after each layer and residual connections.
code: def forward(self, x):
      residual = x
  
      out = self.conv1(x)
      out = self.conv2(out)
      out = self.conv3(out)
  
      if self.downsample is not None:
          residual = self.downsample(x)
  
      out += residual
      out = self.relu(out)
  
      return out
  
imports: import torch.nn
  from torch.hub import load_state_dict_from_url
label_desc: Use image node_id 2 for calling the Bottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: out