original_docstring: r"""This is the slowpath function for Eager mode.
    This is for function _for
    
docstring: r"""This is the slowpath function for Eager mode.
  This is for function _for
docstring_tokens: r " " " This is the slowpath function for Eager mode . This is for function _for
code: def _for_eager_fallback(start, limit, delta, input, body, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function _for
    """
    _ctx = ctx if ctx else _context.context()
    _attr_T, input = _execute.convert_to_mixed_eager_tensors(input, _ctx)
    start = _ops.convert_to_tensor(start, _dtypes.int32)
    limit = _ops.convert_to_tensor(limit, _dtypes.int32)
    delta = _ops.convert_to_tensor(delta, _dtypes.int32)
    _inputs_flat = [start, limit, delta] + list(input)
    _attrs = ("T", _attr_T, "body", body)
    _result = _execute.execute(b"For", len(input), inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "For", _inputs_flat, _attrs, _result, name)
    return _result
code_tokens: def _for_eager_fallback ( start , limit , delta , input , body , name = None , ctx = None ) : _ctx = ctx if ctx else _context . context ( ) _attr_T , input = _execute . convert_to_mixed_eager_tensors ( input , _ctx ) start = _ops . convert_to_tensor ( start , _dtypes . int32 ) limit = _ops . convert_to_tensor ( limit , _dtypes . int32 ) delta = _ops . convert_to_tensor ( delta , _dtypes . int32 ) _inputs_flat = [ start , limit , delta ] + list ( input ) _attrs = ( "T" , _attr_T , "body" , body ) _result = _execute . execute ( b"For" , len ( input ) , inputs = _inputs_flat , attrs = _attrs , ctx = _ctx , name = name ) _execute . record_gradient ( "For" , _inputs_flat , _attrs , _result , name ) return _result
short_docstring: r"""This is the slowpath function for Eager mode.
short_docstring_tokens: r " " " This is the slowpath function for Eager mode .
comment: r"""This is the slowpath function for Eager mode.
    This is for function _for
    """