Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/tensorflow/python/ops/gen_manip_ops.py: 35%

1"""Python wrappers around TensorFlow ops. 

2 

3This file is MACHINE GENERATED! Do not edit. 

4""" 

5 

6import collections 

7 

8from tensorflow.python import pywrap_tfe as pywrap_tfe 

9from tensorflow.python.eager import context as _context 

10from tensorflow.python.eager import core as _core 

11from tensorflow.python.eager import execute as _execute 

12from tensorflow.python.framework import dtypes as _dtypes 

13from tensorflow.security.fuzzing.py import annotation_types as _atypes 

14 

15from tensorflow.python.framework import op_def_registry as _op_def_registry 

16from tensorflow.python.framework import ops as _ops 

17from tensorflow.python.framework import op_def_library as _op_def_library 

18from tensorflow.python.util.deprecation import deprecated_endpoints 

19from tensorflow.python.util import dispatch as _dispatch 

20from tensorflow.python.util.tf_export import tf_export 

21 

22from typing import TypeVar 

23 

24def roll(input, shift, axis, name=None): 

25 r"""Rolls the elements of a tensor along an axis. 

26 

27 The elements are shifted positively (towards larger indices) by the offset of 

28 `shift` along the dimension of `axis`. Negative `shift` values will shift 

29 elements in the opposite direction. Elements that roll passed the last position 

30 will wrap around to the first and vice versa. Multiple shifts along multiple 

31 axes may be specified. 

32 

33 For example: 

34 

35 ``` 

36 # 't' is [0, 1, 2, 3, 4] 

37 roll(t, shift=2, axis=0) ==> [3, 4, 0, 1, 2] 

38 

39 # shifting along multiple dimensions 

40 # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] 

41 roll(t, shift=[1, -2], axis=[0, 1]) ==> [[7, 8, 9, 5, 6], [2, 3, 4, 0, 1]] 

42 

43 # shifting along the same axis multiple times 

44 # 't' is [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] 

45 roll(t, shift=[2, -3], axis=[1, 1]) ==> [[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]] 

46 ``` 

47 

48 Args: 

49 input: A `Tensor`. 

50 shift: A `Tensor`. Must be one of the following types: `int32`, `int64`. 

51 Dimension must be 0-D or 1-D. `shift[i]` specifies the number of places by which 

52 elements are shifted positively (towards larger indices) along the dimension 

53 specified by `axis[i]`. Negative shifts will roll the elements in the opposite 

54 direction. 

55 axis: A `Tensor`. Must be one of the following types: `int32`, `int64`. 

56 Dimension must be 0-D or 1-D. `axis[i]` specifies the dimension that the shift 

57 `shift[i]` should occur. If the same axis is referenced more than once, the 

58 total shift for that axis will be the sum of all the shifts that belong to that 

59 axis. 

60 name: A name for the operation (optional). 

61 

62 Returns: 

63 A `Tensor`. Has the same type as `input`. 

64 """ 

65 _ctx = _context._context or _context.context() 

66 tld = _ctx._thread_local_data 

67 if tld.is_eager: 

68 try: 

69 _result = pywrap_tfe.TFE_Py_FastPathExecute( 

70 _ctx, "Roll", name, input, shift, axis) 

71 return _result 

72 except _core._NotOkStatusException as e: 

73 _ops.raise_from_not_ok_status(e, name) 

74 except _core._FallbackException: 

75 pass 

76 try: 

77 return roll_eager_fallback( 

78 input, shift, axis, name=name, ctx=_ctx) 

79 except _core._SymbolicException: 

80 pass # Add nodes to the TensorFlow graph. 

81 # Add nodes to the TensorFlow graph. 

82 _, _, _op, _outputs = _op_def_library._apply_op_helper( 

83 "Roll", input=input, shift=shift, axis=axis, name=name) 

84 _result = _outputs[:] 

85 if _execute.must_record_gradient(): 

86 _attrs = ("T", _op._get_attr_type("T"), "Tshift", 

87 _op._get_attr_type("Tshift"), "Taxis", 

88 _op._get_attr_type("Taxis")) 

89 _inputs_flat = _op.inputs 

90 _execute.record_gradient( 

91 "Roll", _inputs_flat, _attrs, _result) 

92 _result, = _result 

93 return _result 

94 

95Roll = tf_export("raw_ops.Roll")(_ops.to_raw_op(roll)) 

96 

97 

98def roll_eager_fallback(input, shift, axis, name, ctx): 

99 _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, []) 

100 _attr_Tshift, (shift,) = _execute.args_to_matching_eager([shift], ctx, [_dtypes.int32, _dtypes.int64, ]) 

101 _attr_Taxis, (axis,) = _execute.args_to_matching_eager([axis], ctx, [_dtypes.int32, _dtypes.int64, ]) 

102 _inputs_flat = [input, shift, axis] 

103 _attrs = ("T", _attr_T, "Tshift", _attr_Tshift, "Taxis", _attr_Taxis) 

104 _result = _execute.execute(b"Roll", 1, inputs=_inputs_flat, attrs=_attrs, 

105 ctx=ctx, name=name) 

106 if _execute.must_record_gradient(): 

107 _execute.record_gradient( 

108 "Roll", _inputs_flat, _attrs, _result) 

109 _result, = _result 

110 return _result 

111