original_docstring: r"""This is the slowpath function for Eager mode.
    This is for function case
    
docstring: r"""This is the slowpath function for Eager mode.
  This is for function case
docstring_tokens: r " " " This is the slowpath function for Eager mode . This is for function case
code: def case_eager_fallback(branch_index, input, Tout, branches, output_shapes=[], name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function case
    """
    _ctx = ctx if ctx else _context.context()
    if not isinstance(Tout, (list, tuple)):
      raise TypeError(
          "Expected list for 'Tout' argument to "
          "'case' Op, not %r." % Tout)
    Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
    if not isinstance(branches, (list, tuple)):
      raise TypeError(
          "Expected list for 'branches' argument to "
          "'case' Op, not %r." % branches)
    if output_shapes is None:
      output_shapes = []
    if not isinstance(output_shapes, (list, tuple)):
      raise TypeError(
          "Expected list for 'output_shapes' argument to "
          "'case' Op, not %r." % output_shapes)
    output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
    _attr_Tin, input = _execute.convert_to_mixed_eager_tensors(input, _ctx)
    branch_index = _ops.convert_to_tensor(branch_index, _dtypes.int32)
    _inputs_flat = [branch_index] + list(input)
    _attrs = ("Tin", _attr_Tin, "Tout", Tout, "branches", branches,
    "output_shapes", output_shapes)
    _result = _execute.execute(b"Case", len(Tout), inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "Case", _inputs_flat, _attrs, _result, name)
    return _result
code_tokens: def case_eager_fallback ( branch_index , input , Tout , branches , output_shapes = [ ] , name = None , ctx = None ) : _ctx = ctx if ctx else _context . context ( ) if not isinstance ( Tout , ( list , tuple ) ) : raise TypeError ( "Expected list for 'Tout' argument to " "'case' Op, not %r." % Tout ) Tout = [ _execute . make_type ( _t , "Tout" ) for _t in Tout ] if not isinstance ( branches , ( list , tuple ) ) : raise TypeError ( "Expected list for 'branches' argument to " "'case' Op, not %r." % branches ) if output_shapes is None : output_shapes = [ ] if not isinstance ( output_shapes , ( list , tuple ) ) : raise TypeError ( "Expected list for 'output_shapes' argument to " "'case' Op, not %r." % output_shapes ) output_shapes = [ _execute . make_shape ( _s , "output_shapes" ) for _s in output_shapes ] _attr_Tin , input = _execute . convert_to_mixed_eager_tensors ( input , _ctx ) branch_index = _ops . convert_to_tensor ( branch_index , _dtypes . int32 ) _inputs_flat = [ branch_index ] + list ( input ) _attrs = ( "Tin" , _attr_Tin , "Tout" , Tout , "branches" , branches , "output_shapes" , output_shapes ) _result = _execute . execute ( b"Case" , len ( Tout ) , inputs = _inputs_flat , attrs = _attrs , ctx = _ctx , name = name ) _execute . record_gradient ( "Case" , _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 case
    """