parameters: (self,
                 opt,
                 vars_to_clip_dims,
                 max_norm,
                 use_locking=False,
                 colocate_clip_ops_with_vars=False,
                 name="VariableClipping")
docstring: Construct a new clip-norm optimizer.
  
      Args:
        opt: The actual optimizer that will be used to compute and apply the
          gradients. Must be one of the Optimizer classes.
        vars_to_clip_dims: A dict with keys as Variables and values as lists
          of dimensions along which to compute the L2-norm.  See
          `tf.clip_by_norm` for more details.
        max_norm: The L2-norm to clip to, for all variables specified.
        use_locking: If `True` use locks for clip update operations.
        colocate_clip_ops_with_vars: If `True`, try colocating the clip norm
          ops with the corresponding variable.
        name: Optional name prefix for the operations created when applying
          gradients.  Defaults to "VariableClipping".
docstring_summary: Construct a new clip-norm optimizer.
docstring_tokens: Construct a new clip - norm optimizer .
function: def __init__(self,
                 opt,
                 vars_to_clip_dims,
                 max_norm,
                 use_locking=False,
                 colocate_clip_ops_with_vars=False,
                 name="VariableClipping"):
      """Construct a new clip-norm optimizer.
  
      Args:
        opt: The actual optimizer that will be used to compute and apply the
          gradients. Must be one of the Optimizer classes.
        vars_to_clip_dims: A dict with keys as Variables and values as lists
          of dimensions along which to compute the L2-norm.  See
          `tf.clip_by_norm` for more details.
        max_norm: The L2-norm to clip to, for all variables specified.
        use_locking: If `True` use locks for clip update operations.
        colocate_clip_ops_with_vars: If `True`, try colocating the clip norm
          ops with the corresponding variable.
        name: Optional name prefix for the operations created when applying
          gradients.  Defaults to "VariableClipping".
      """
      super(VariableClippingOptimizer, self).__init__(use_locking, name)
      self._opt = opt
      # Defensive copy of input dict
      self._vars_to_clip_dims = {
          var: clip_dims[:] for var, clip_dims in vars_to_clip_dims.items()}
      self._max_norm = max_norm
      self._colocate_clip_ops_with_vars = colocate_clip_ops_with_vars
function_tokens: def __init__ ( self , opt , vars_to_clip_dims , max_norm , use_locking = False , colocate_clip_ops_with_vars = False , name = "VariableClipping" ) : super ( VariableClippingOptimizer , self ) . __init__ ( use_locking , name ) self . _opt = opt # Defensive copy of input dict self . _vars_to_clip_dims = { var : clip_dims [ : ] for var , clip_dims in vars_to_clip_dims . items ( ) } self . _max_norm = max_norm self . _colocate_clip_ops_with_vars = colocate_clip_ops_with_vars