original_docstring: 
          Adds a dimension for the capsules in the conv output. In the original paper, squash activation function was applied right after the dimension
          expansion took place. However, in the official implementation, no squashing was applied. Here we'll try both the implementation and see what comes
          on top. The activation function is what ensures the discriminative learning to treat the tensors as capsules.
          
docstring: Adds a dimension for the capsules in the conv output. In the original paper, squash activation function was applied right after the dimension
  expansion took place. However, in the official implementation, no squashing was applied. Here we'll try both the implementation and see what comes
  on top. The activation function is what ensures the discriminative learning to treat the tensors as capsules.
docstring_tokens: Adds a dimension for the capsules in the conv output . In the original paper squash activation function was applied right after the dimension expansion took place . However in the official implementation no squashing was applied . Here we ' ll try both the implementation and see what comes on top . The activation function is what ensures the discriminative learning to treat the tensors as capsules .
code: def forward(self, x):
          '''
          Adds a dimension for the capsules in the conv output. In the original paper, squash activation function was applied right after the dimension
          expansion took place. However, in the official implementation, no squashing was applied. Here we'll try both the implementation and see what comes
          on top. The activation function is what ensures the discriminative learning to treat the tensors as capsules.
          '''
          return squash(torch.unsqueeze(x, dim=2), dim=2)
code_tokens: def forward ( self , x ) : return squash ( torch . unsqueeze ( x , dim = 2 ) , dim = 2 )
short_docstring: Adds a dimension for the capsules in the conv output.
short_docstring_tokens: Adds a dimension for the capsules in the conv output .
comment: '''
          Adds a dimension for the capsules in the conv output. In the original paper, squash activation function was applied right after the dimension
          expansion took place. However, in the official implementation, no squashing was applied. Here we'll try both the implementation and see what comes
          on top. The activation function is what ensures the discriminative learning to treat the tensors as capsules.
          '''