parameters: (objects, kwargs)
return_statement: return py_func.wrap_py_func(
        print_wrapper, None, objects, use_dummy_return=True)
docstring: Overload of print_ as a py_func implementation.
docstring_summary: Overload of print_ as a py_func implementation.
docstring_tokens: Overload of print_ as a py_func implementation .
function: def _tf_py_func_print(objects, kwargs):
    """Overload of print_ as a py_func implementation."""
    override_kwargs = {k: v for k, v in kwargs.items() if v is not UNSPECIFIED}
    if 'flush' not in override_kwargs:
      # Defaulting to flushing the console in graph mode, which helps reduce
      # garbled output in IPython.
      override_kwargs['flush'] = True
  
    def print_wrapper(*vals):
      vals = tuple(v.numpy() if tensor_util.is_tf_type(v) else v for v in vals)
      # TensorFlow doesn't seem to generate Unicode when passing strings to
      # py_func. This causes the print to add a "b'" wrapper to the output,
      # which is probably never what you want.
      vals = tuple(v.decode('utf-8') if isinstance(v, bytes) else v for v in vals)
      six.print_(*vals, **override_kwargs)
  
    return py_func.wrap_py_func(
        print_wrapper, None, objects, use_dummy_return=True)
function_tokens: def _tf_py_func_print ( objects , kwargs ) : override_kwargs = { k : v for k , v in kwargs . items ( ) if v is not UNSPECIFIED } if 'flush' not in override_kwargs : # Defaulting to flushing the console in graph mode, which helps reduce # garbled output in IPython. override_kwargs [ 'flush' ] = True def print_wrapper ( * vals ) : vals = tuple ( v . numpy ( ) if tensor_util . is_tf_type ( v ) else v for v in vals ) # TensorFlow doesn't seem to generate Unicode when passing strings to # py_func. This causes the print to add a "b'" wrapper to the output, # which is probably never what you want. vals = tuple ( v . decode ( 'utf-8' ) if isinstance ( v , bytes ) else v for v in vals ) six . print_ ( * vals , * * override_kwargs ) return py_func . wrap_py_func ( print_wrapper , None , objects , use_dummy_return = True )