parameters: (node, context)
return_statement: return node
docstring: Convert Python code to equivalent TF graph mode code.
  
    Args:
      node: AST, the code to convert.
      context: converter.EntityContext
  
    Returns:
      A tuple (node, deps):
          * node: A Python ast node, representing the converted code.
          * deps: A set of strings, the fully qualified names of entity
              dependencies that this node has.
docstring_summary: Convert Python code to equivalent TF graph mode code.
docstring_tokens: Convert Python code to equivalent TF graph mode code .
function: def node_to_graph(node, context):
    """Convert Python code to equivalent TF graph mode code.
  
    Args:
      node: AST, the code to convert.
      context: converter.EntityContext
  
    Returns:
      A tuple (node, deps):
          * node: A Python ast node, representing the converted code.
          * deps: A set of strings, the fully qualified names of entity
              dependencies that this node has.
    """
    # TODO(mdan): Insert list_comprehensions somewhere.
    unsupported_features_checker.verify(node)
  
    node = converter.standard_analysis(node, context, is_initial=True)
    node = converter.apply_(node, context, function_scopes)
    node = converter.apply_(node, context, arg_defaults)
    node = converter.apply_(node, context, directives)
    node = converter.apply_(node, context, break_statements)
    if context.program.options.uses(converter.Feature.ASSERT_STATEMENTS):
      node = converter.apply_(node, context, asserts)
    # Note: sequencing continue canonicalization before for loop one avoids
    # dealing with the extra loop increment operation that the for
    # canonicalization creates.
    node = converter.apply_(node, context, continue_statements)
    node = converter.apply_(node, context, return_statements)
    if context.program.options.uses(converter.Feature.LISTS):
      node = converter.apply_(node, context, lists)
      node = converter.apply_(node, context, slices)
    node = converter.apply_(node, context, call_trees)
    node = converter.apply_(node, context, control_flow)
    node = converter.apply_(node, context, conditional_expressions)
    node = converter.apply_(node, context, logical_expressions)
    return node
function_tokens: def node_to_graph ( node , context ) : # TODO(mdan): Insert list_comprehensions somewhere. unsupported_features_checker . verify ( node ) node = converter . standard_analysis ( node , context , is_initial = True ) node = converter . apply_ ( node , context , function_scopes ) node = converter . apply_ ( node , context , arg_defaults ) node = converter . apply_ ( node , context , directives ) node = converter . apply_ ( node , context , break_statements ) if context . program . options . uses ( converter . Feature . ASSERT_STATEMENTS ) : node = converter . apply_ ( node , context , asserts ) # Note: sequencing continue canonicalization before for loop one avoids # dealing with the extra loop increment operation that the for # canonicalization creates. node = converter . apply_ ( node , context , continue_statements ) node = converter . apply_ ( node , context , return_statements ) if context . program . options . uses ( converter . Feature . LISTS ) : node = converter . apply_ ( node , context , lists ) node = converter . apply_ ( node , context , slices ) node = converter . apply_ ( node , context , call_trees ) node = converter . apply_ ( node , context , control_flow ) node = converter . apply_ ( node , context , conditional_expressions ) node = converter . apply_ ( node , context , logical_expressions ) return node