parameters: (graph=None)
docstring: Create global step tensor in graph.
  
    Args:
      graph: The graph in which to create the global step. If missing, use default
          graph.
  
    Returns:
      Global step tensor.
  
    Raises:
      ValueError: if global step key is already defined.
docstring_summary: Create global step tensor in graph.
docstring_tokens: Create global step tensor in graph .
function: def create_global_step(graph=None):
    """Create global step tensor in graph.
  
    Args:
      graph: The graph in which to create the global step. If missing, use default
          graph.
  
    Returns:
      Global step tensor.
  
    Raises:
      ValueError: if global step key is already defined.
    """
    graph = ops.get_default_graph() if graph is None else graph
    if get_global_step(graph) is not None:
      raise ValueError('"global_step" already exists.')
    # Create in proper graph and base name_scope.
    with graph.as_default() as g, g.name_scope(None):
      collections = [ops.GraphKeys.VARIABLES, ops.GraphKeys.GLOBAL_STEP]
      return variable(ops.GraphKeys.GLOBAL_STEP, shape=[], dtype=dtypes.int64,
                      initializer=init_ops.zeros_initializer, trainable=False,
                      collections=collections)
function_tokens: def create_global_step ( graph = None ) : graph = ops . get_default_graph ( ) if graph is None else graph if get_global_step ( graph ) is not None : raise ValueError ( '"global_step" already exists.' ) # Create in proper graph and base name_scope. with graph . as_default ( ) as g , g . name_scope ( None ) : collections = [ ops . GraphKeys . VARIABLES , ops . GraphKeys . GLOBAL_STEP ] return variable ( ops . GraphKeys . GLOBAL_STEP , shape = [ ] , dtype = dtypes . int64 , initializer = init_ops . zeros_initializer , trainable = False , collections = collections )