parameters: (self, x)
return_statement: return b, a
docstring: Arguments:
              x: a float tensor with shape [batch_size, 3, h, w].
          Returns:
              b: a float tensor with shape [batch_size, 4].
              a: a float tensor with shape [batch_size, 2].
docstring_summary: Arguments:
              x: a float tensor with shape [batch_size, 3, h, w].
          Returns:
              b: a float tensor with shape [batch_size, 4].
              a: a float tensor with shape [batch_size, 2].
docstring_tokens: Arguments : x : a float tensor with shape [ batch_size 3 h w ] . Returns : b : a float tensor with shape [ batch_size 4 ] . a : a float tensor with shape [ batch_size 2 ] .
function: def forward(self, x):
          """
          Arguments:
              x: a float tensor with shape [batch_size, 3, h, w].
          Returns:
              b: a float tensor with shape [batch_size, 4].
              a: a float tensor with shape [batch_size, 2].
          """
          x = self.features(x)
          a = self.conv5_1(x)
          b = self.conv5_2(x)
  
          if self.is_train is False:
              a = F.softmax(a, dim=1)
  
          return b, a
function_tokens: def forward ( self , x ) : x = self . features ( x ) a = self . conv5_1 ( x ) b = self . conv5_2 ( x ) if self . is_train is False : a = F . softmax ( a , dim = 1 ) return b , a