parameters: (self, c)
return_statement: return c.view(*expected_view_shape)
docstring: Broadcasts a correction factor to the output for elementwise operations.
  
      Two tensors are “broadcastable” if the following rules hold:
        - Each tensor has at least one dimension.
        - When iterating over the dimension sizes, starting at the trailing
          dimension, the dimension sizes must either be equal,
          one of them is 1, or one of them does not exist.
      See https://pytorch.org/docs/stable/notes/broadcasting.html
docstring_summary: Broadcasts a correction factor to the output for elementwise operations.
docstring_tokens: Broadcasts a correction factor to the output for elementwise operations .
function: def broadcast_correction(self, c):
      """Broadcasts a correction factor to the output for elementwise operations.
  
      Two tensors are “broadcastable” if the following rules hold:
        - Each tensor has at least one dimension.
        - When iterating over the dimension sizes, starting at the trailing
          dimension, the dimension sizes must either be equal,
          one of them is 1, or one of them does not exist.
      See https://pytorch.org/docs/stable/notes/broadcasting.html
      """
      expected_output_dim = self.dim + 2
      view_fillers_dim = expected_output_dim - c.dim() - 1
      view_filler = (1,) * view_fillers_dim
      expected_view_shape = c.shape + view_filler
      return c.view(*expected_view_shape)
function_tokens: def broadcast_correction ( self , c ) : expected_output_dim = self . dim + 2 view_fillers_dim = expected_output_dim - c . dim ( ) - 1 view_filler = ( 1 , ) * view_fillers_dim expected_view_shape = c . shape + view_filler return c . view ( * expected_view_shape )