parameters: (self, is_row_sweep_var, train_ops, num_rows, num_cols,
                 input_row_indices, input_col_indices, row_prep_ops,
                 col_prep_ops, init_op, completed_sweeps_var)
docstring: Initializes SweepHook.
  
      Args:
        is_row_sweep_var: A Boolean tf.Variable, determines whether we are
          currently doing a row or column sweep. It is updated by the hook.
        train_ops: A list of ops. The ops created by this hook will have
          control dependencies on `train_ops`.
        num_rows: int, the total number of rows to be processed.
        num_cols: int, the total number of columns to be processed.
        input_row_indices: A Tensor of type int64. The indices of the input rows
          that are processed during the current sweep. All elements of
          `input_row_indices` must be in [0, num_rows).
        input_col_indices: A Tensor of type int64. The indices of the input
          columns that are processed during the current sweep. All elements of
          `input_col_indices` must be in [0, num_cols).
        row_prep_ops: list of ops, to be run before the beginning of each row
          sweep, in the given order.
        col_prep_ops: list of ops, to be run before the beginning of each column
          sweep, in the given order.
        init_op: op to be run once before training. This is typically a local
          initialization op (such as cache initialization).
        completed_sweeps_var: An integer tf.Variable, indicates the number of
          completed sweeps. It is updated by the hook.
docstring_summary: Initializes SweepHook.
docstring_tokens: Initializes SweepHook .
function: def __init__(self, is_row_sweep_var, train_ops, num_rows, num_cols,
                 input_row_indices, input_col_indices, row_prep_ops,
                 col_prep_ops, init_op, completed_sweeps_var):
      """Initializes SweepHook.
  
      Args:
        is_row_sweep_var: A Boolean tf.Variable, determines whether we are
          currently doing a row or column sweep. It is updated by the hook.
        train_ops: A list of ops. The ops created by this hook will have
          control dependencies on `train_ops`.
        num_rows: int, the total number of rows to be processed.
        num_cols: int, the total number of columns to be processed.
        input_row_indices: A Tensor of type int64. The indices of the input rows
          that are processed during the current sweep. All elements of
          `input_row_indices` must be in [0, num_rows).
        input_col_indices: A Tensor of type int64. The indices of the input
          columns that are processed during the current sweep. All elements of
          `input_col_indices` must be in [0, num_cols).
        row_prep_ops: list of ops, to be run before the beginning of each row
          sweep, in the given order.
        col_prep_ops: list of ops, to be run before the beginning of each column
          sweep, in the given order.
        init_op: op to be run once before training. This is typically a local
          initialization op (such as cache initialization).
        completed_sweeps_var: An integer tf.Variable, indicates the number of
          completed sweeps. It is updated by the hook.
      """
      self._num_rows = num_rows
      self._num_cols = num_cols
      self._row_prep_ops = row_prep_ops
      self._col_prep_ops = col_prep_ops
      self._init_op = init_op
      self._is_row_sweep_var = is_row_sweep_var
      self._completed_sweeps_var = completed_sweeps_var
      # Boolean variable that determines whether the init_ops have been run.
      self._is_initialized = False
      # Ops to run jointly with train_ops, responsible for updating
      # `is_row_sweep_var` and incrementing the `global_step` and
      # `completed_sweeps` counters.
      self._update_op, self._is_sweep_done_var, self._switch_op = (
          self._create_hook_ops(input_row_indices, input_col_indices, train_ops))
function_tokens: def __init__ ( self , is_row_sweep_var , train_ops , num_rows , num_cols , input_row_indices , input_col_indices , row_prep_ops , col_prep_ops , init_op , completed_sweeps_var ) : self . _num_rows = num_rows self . _num_cols = num_cols self . _row_prep_ops = row_prep_ops self . _col_prep_ops = col_prep_ops self . _init_op = init_op self . _is_row_sweep_var = is_row_sweep_var self . _completed_sweeps_var = completed_sweeps_var # Boolean variable that determines whether the init_ops have been run. self . _is_initialized = False # Ops to run jointly with train_ops, responsible for updating # `is_row_sweep_var` and incrementing the `global_step` and # `completed_sweeps` counters. self . _update_op , self . _is_sweep_done_var , self . _switch_op = ( self . _create_hook_ops ( input_row_indices , input_col_indices , train_ops ) )