parameters: (self, sp_input=None, transpose_input=False)
return_statement: return self._process_input_helper(
          False, sp_input=sp_input, transpose_input=transpose_input)
docstring: r"""Updates the column factors.
  
      Args:
        sp_input: A SparseTensor representing a subset of columns of the full
          input. Please refer to comments for update_row_factors for
          restrictions.
        transpose_input: If true, the input will be logically transposed and the
          columns corresponding to the transposed input are updated.
  
      Returns:
        A tuple consisting of the following elements:
        new_values: New values for the column factors.
        update_op: An op that assigns the newly computed values to the column
          factors.
        unregularized_loss: A tensor (scalar) that contains the normalized
          minibatch loss corresponding to sp_input, without the regularization
          term. If sp_input contains the columns {A_{:, j}, j \in J}, and the
          input matrix A has m total columns, then the unregularized loss is:
          (\|\sqrt W_J \odot (A_J - U V_J^T)\|_F^2 * m / |I|
          The total loss is unregularized_loss + regularization.
        regularization: A tensor (scalar) that contains the normalized
          regularization term for the minibatch loss corresponding to sp_input.
          If sp_input contains the columns {A_{:, j}, j \in J}, and the input
          matrix A has m total columns, then the regularization term is:
          \lambda \|V_J\|_F^2) * m / |J| + \lambda \|U\|_F^2.
        sum_weights: The sum of the weights W_J corresponding to sp_input,
          normalized by a factor of m / |J|. The root weighted squared error is:
          \sqrt(unregularized_loss / sum_weights).
docstring_summary: r"""Updates the column factors.
docstring_tokens: r Updates the column factors .
function: def update_col_factors(self, sp_input=None, transpose_input=False):
      r"""Updates the column factors.
  
      Args:
        sp_input: A SparseTensor representing a subset of columns of the full
          input. Please refer to comments for update_row_factors for
          restrictions.
        transpose_input: If true, the input will be logically transposed and the
          columns corresponding to the transposed input are updated.
  
      Returns:
        A tuple consisting of the following elements:
        new_values: New values for the column factors.
        update_op: An op that assigns the newly computed values to the column
          factors.
        unregularized_loss: A tensor (scalar) that contains the normalized
          minibatch loss corresponding to sp_input, without the regularization
          term. If sp_input contains the columns {A_{:, j}, j \in J}, and the
          input matrix A has m total columns, then the unregularized loss is:
          (\|\sqrt W_J \odot (A_J - U V_J^T)\|_F^2 * m / |I|
          The total loss is unregularized_loss + regularization.
        regularization: A tensor (scalar) that contains the normalized
          regularization term for the minibatch loss corresponding to sp_input.
          If sp_input contains the columns {A_{:, j}, j \in J}, and the input
          matrix A has m total columns, then the regularization term is:
          \lambda \|V_J\|_F^2) * m / |J| + \lambda \|U\|_F^2.
        sum_weights: The sum of the weights W_J corresponding to sp_input,
          normalized by a factor of m / |J|. The root weighted squared error is:
          \sqrt(unregularized_loss / sum_weights).
      """
      return self._process_input_helper(
          False, sp_input=sp_input, transpose_input=transpose_input)
function_tokens: def update_col_factors ( self , sp_input = None , transpose_input = False ) : return self . _process_input_helper ( False , sp_input = sp_input , transpose_input = transpose_input )