1// This file is MACHINE GENERATED! Do not edit.
2
3
4#include "tensorflow/cc/ops/const_op.h"
5#include "tensorflow/cc/ops/math_ops_internal.h"
6
7namespace tensorflow {
8namespace ops {
9namespace internal {
10// NOTE: This namespace has internal TensorFlow details that
11// are not part of TensorFlow's public API.
12
13CumulativeLogsumexp::CumulativeLogsumexp(const ::tensorflow::Scope& scope,
14 ::tensorflow::Input x,
15 ::tensorflow::Input axis, const
16 CumulativeLogsumexp::Attrs& attrs) {
17 if (!scope.ok()) return;
18 auto _x = ::tensorflow::ops::AsNodeOut(scope, x);
19 if (!scope.ok()) return;
20 auto _axis = ::tensorflow::ops::AsNodeOut(scope, axis);
21 if (!scope.ok()) return;
22 ::tensorflow::Node* ret;
23 const auto unique_name = scope.GetUniqueNameForOp("CumulativeLogsumexp");
24 auto builder = ::tensorflow::NodeBuilder(unique_name, "CumulativeLogsumexp")
25 .Input(_x)
26 .Input(_axis)
27 .Attr("exclusive", attrs.exclusive_)
28 .Attr("reverse", attrs.reverse_)
29 ;
30 scope.UpdateBuilder(&builder);
31 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
32 if (!scope.ok()) return;
33 scope.UpdateStatus(scope.DoShapeInference(ret));
34 this->operation = Operation(ret);
35 this->out = Output(ret, 0);
36}
37
38CumulativeLogsumexp::CumulativeLogsumexp(const ::tensorflow::Scope& scope,
39 ::tensorflow::Input x,
40 ::tensorflow::Input axis)
41 : CumulativeLogsumexp(scope, x, axis, CumulativeLogsumexp::Attrs()) {}
42
43IgammaGradA::IgammaGradA(const ::tensorflow::Scope& scope, ::tensorflow::Input
44 a, ::tensorflow::Input x) {
45 if (!scope.ok()) return;
46 auto _a = ::tensorflow::ops::AsNodeOut(scope, a);
47 if (!scope.ok()) return;
48 auto _x = ::tensorflow::ops::AsNodeOut(scope, x);
49 if (!scope.ok()) return;
50 ::tensorflow::Node* ret;
51 const auto unique_name = scope.GetUniqueNameForOp("IgammaGradA");
52 auto builder = ::tensorflow::NodeBuilder(unique_name, "IgammaGradA")
53 .Input(_a)
54 .Input(_x)
55 ;
56 scope.UpdateBuilder(&builder);
57 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
58 if (!scope.ok()) return;
59 scope.UpdateStatus(scope.DoShapeInference(ret));
60 this->operation = Operation(ret);
61 this->z = Output(ret, 0);
62}
63
64InvGrad::InvGrad(const ::tensorflow::Scope& scope, ::tensorflow::Input y,
65 ::tensorflow::Input dy) {
66 if (!scope.ok()) return;
67 auto _y = ::tensorflow::ops::AsNodeOut(scope, y);
68 if (!scope.ok()) return;
69 auto _dy = ::tensorflow::ops::AsNodeOut(scope, dy);
70 if (!scope.ok()) return;
71 ::tensorflow::Node* ret;
72 const auto unique_name = scope.GetUniqueNameForOp("InvGrad");
73 auto builder = ::tensorflow::NodeBuilder(unique_name, "InvGrad")
74 .Input(_y)
75 .Input(_dy)
76 ;
77 scope.UpdateBuilder(&builder);
78 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
79 if (!scope.ok()) return;
80 scope.UpdateStatus(scope.DoShapeInference(ret));
81 this->operation = Operation(ret);
82 this->z = Output(ret, 0);
83}
84
85LinSpace::LinSpace(const ::tensorflow::Scope& scope, ::tensorflow::Input start,
86 ::tensorflow::Input stop, ::tensorflow::Input num) {
87 if (!scope.ok()) return;
88 auto _start = ::tensorflow::ops::AsNodeOut(scope, start);
89 if (!scope.ok()) return;
90 auto _stop = ::tensorflow::ops::AsNodeOut(scope, stop);
91 if (!scope.ok()) return;
92 auto _num = ::tensorflow::ops::AsNodeOut(scope, num);
93 if (!scope.ok()) return;
94 ::tensorflow::Node* ret;
95 const auto unique_name = scope.GetUniqueNameForOp("LinSpace");
96 auto builder = ::tensorflow::NodeBuilder(unique_name, "LinSpace")
97 .Input(_start)
98 .Input(_stop)
99 .Input(_num)
100 ;
101 scope.UpdateBuilder(&builder);
102 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
103 if (!scope.ok()) return;
104 scope.UpdateStatus(scope.DoShapeInference(ret));
105 this->operation = Operation(ret);
106 this->output = Output(ret, 0);
107}
108
109ReciprocalGrad::ReciprocalGrad(const ::tensorflow::Scope& scope,
110 ::tensorflow::Input y, ::tensorflow::Input dy) {
111 if (!scope.ok()) return;
112 auto _y = ::tensorflow::ops::AsNodeOut(scope, y);
113 if (!scope.ok()) return;
114 auto _dy = ::tensorflow::ops::AsNodeOut(scope, dy);
115 if (!scope.ok()) return;
116 ::tensorflow::Node* ret;
117 const auto unique_name = scope.GetUniqueNameForOp("ReciprocalGrad");
118 auto builder = ::tensorflow::NodeBuilder(unique_name, "ReciprocalGrad")
119 .Input(_y)
120 .Input(_dy)
121 ;
122 scope.UpdateBuilder(&builder);
123 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
124 if (!scope.ok()) return;
125 scope.UpdateStatus(scope.DoShapeInference(ret));
126 this->operation = Operation(ret);
127 this->z = Output(ret, 0);
128}
129
130RequantizationRangePerChannel::RequantizationRangePerChannel(const
131 ::tensorflow::Scope&
132 scope,
133 ::tensorflow::Input
134 input,
135 ::tensorflow::Input
136 input_min,
137 ::tensorflow::Input
138 input_max, float
139 clip_value_max) {
140 if (!scope.ok()) return;
141 auto _input = ::tensorflow::ops::AsNodeOut(scope, input);
142 if (!scope.ok()) return;
143 auto _input_min = ::tensorflow::ops::AsNodeOut(scope, input_min);
144 if (!scope.ok()) return;
145 auto _input_max = ::tensorflow::ops::AsNodeOut(scope, input_max);
146 if (!scope.ok()) return;
147 ::tensorflow::Node* ret;
148 const auto unique_name = scope.GetUniqueNameForOp("RequantizationRangePerChannel");
149 auto builder = ::tensorflow::NodeBuilder(unique_name, "RequantizationRangePerChannel")
150 .Input(_input)
151 .Input(_input_min)
152 .Input(_input_max)
153 .Attr("clip_value_max", clip_value_max)
154 ;
155 scope.UpdateBuilder(&builder);
156 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
157 if (!scope.ok()) return;
158 scope.UpdateStatus(scope.DoShapeInference(ret));
159 this->operation = Operation(ret);
160 ::tensorflow::NameRangeMap _outputs_range;
161 ::tensorflow::Status _status_ = ::tensorflow::NameRangesForNode(*ret, ret->op_def(), nullptr, &_outputs_range);
162 if (!_status_.ok()) {
163 scope.UpdateStatus(_status_);
164 return;
165 }
166
167 this->output_min = Output(ret, _outputs_range["output_min"].first);
168 this->output_max = Output(ret, _outputs_range["output_max"].first);
169}
170
171RequantizePerChannel::RequantizePerChannel(const ::tensorflow::Scope& scope,
172 ::tensorflow::Input input,
173 ::tensorflow::Input input_min,
174 ::tensorflow::Input input_max,
175 ::tensorflow::Input
176 requested_output_min,
177 ::tensorflow::Input
178 requested_output_max, const
179 RequantizePerChannel::Attrs& attrs) {
180 if (!scope.ok()) return;
181 auto _input = ::tensorflow::ops::AsNodeOut(scope, input);
182 if (!scope.ok()) return;
183 auto _input_min = ::tensorflow::ops::AsNodeOut(scope, input_min);
184 if (!scope.ok()) return;
185 auto _input_max = ::tensorflow::ops::AsNodeOut(scope, input_max);
186 if (!scope.ok()) return;
187 auto _requested_output_min = ::tensorflow::ops::AsNodeOut(scope, requested_output_min);
188 if (!scope.ok()) return;
189 auto _requested_output_max = ::tensorflow::ops::AsNodeOut(scope, requested_output_max);
190 if (!scope.ok()) return;
191 ::tensorflow::Node* ret;
192 const auto unique_name = scope.GetUniqueNameForOp("RequantizePerChannel");
193 auto builder = ::tensorflow::NodeBuilder(unique_name, "RequantizePerChannel")
194 .Input(_input)
195 .Input(_input_min)
196 .Input(_input_max)
197 .Input(_requested_output_min)
198 .Input(_requested_output_max)
199 .Attr("out_type", attrs.out_type_)
200 ;
201 scope.UpdateBuilder(&builder);
202 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
203 if (!scope.ok()) return;
204 scope.UpdateStatus(scope.DoShapeInference(ret));
205 this->operation = Operation(ret);
206 ::tensorflow::NameRangeMap _outputs_range;
207 ::tensorflow::Status _status_ = ::tensorflow::NameRangesForNode(*ret, ret->op_def(), nullptr, &_outputs_range);
208 if (!_status_.ok()) {
209 scope.UpdateStatus(_status_);
210 return;
211 }
212
213 this->output = Output(ret, _outputs_range["output"].first);
214 this->output_min = Output(ret, _outputs_range["output_min"].first);
215 this->output_max = Output(ret, _outputs_range["output_max"].first);
216}
217
218RequantizePerChannel::RequantizePerChannel(const ::tensorflow::Scope& scope,
219 ::tensorflow::Input input,
220 ::tensorflow::Input input_min,
221 ::tensorflow::Input input_max,
222 ::tensorflow::Input
223 requested_output_min,
224 ::tensorflow::Input
225 requested_output_max)
226 : RequantizePerChannel(scope, input, input_min, input_max, requested_output_min, requested_output_max, RequantizePerChannel::Attrs()) {}
227
228RsqrtGrad::RsqrtGrad(const ::tensorflow::Scope& scope, ::tensorflow::Input y,
229 ::tensorflow::Input dy) {
230 if (!scope.ok()) return;
231 auto _y = ::tensorflow::ops::AsNodeOut(scope, y);
232 if (!scope.ok()) return;
233 auto _dy = ::tensorflow::ops::AsNodeOut(scope, dy);
234 if (!scope.ok()) return;
235 ::tensorflow::Node* ret;
236 const auto unique_name = scope.GetUniqueNameForOp("RsqrtGrad");
237 auto builder = ::tensorflow::NodeBuilder(unique_name, "RsqrtGrad")
238 .Input(_y)
239 .Input(_dy)
240 ;
241 scope.UpdateBuilder(&builder);
242 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
243 if (!scope.ok()) return;
244 scope.UpdateStatus(scope.DoShapeInference(ret));
245 this->operation = Operation(ret);
246 this->z = Output(ret, 0);
247}
248
249SigmoidGrad::SigmoidGrad(const ::tensorflow::Scope& scope, ::tensorflow::Input
250 y, ::tensorflow::Input dy) {
251 if (!scope.ok()) return;
252 auto _y = ::tensorflow::ops::AsNodeOut(scope, y);
253 if (!scope.ok()) return;
254 auto _dy = ::tensorflow::ops::AsNodeOut(scope, dy);
255 if (!scope.ok()) return;
256 ::tensorflow::Node* ret;
257 const auto unique_name = scope.GetUniqueNameForOp("SigmoidGrad");
258 auto builder = ::tensorflow::NodeBuilder(unique_name, "SigmoidGrad")
259 .Input(_y)
260 .Input(_dy)
261 ;
262 scope.UpdateBuilder(&builder);
263 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
264 if (!scope.ok()) return;
265 scope.UpdateStatus(scope.DoShapeInference(ret));
266 this->operation = Operation(ret);
267 this->z = Output(ret, 0);
268}
269
270SobolSample::SobolSample(const ::tensorflow::Scope& scope, ::tensorflow::Input
271 dim, ::tensorflow::Input num_results,
272 ::tensorflow::Input skip, const SobolSample::Attrs&
273 attrs) {
274 if (!scope.ok()) return;
275 auto _dim = ::tensorflow::ops::AsNodeOut(scope, dim);
276 if (!scope.ok()) return;
277 auto _num_results = ::tensorflow::ops::AsNodeOut(scope, num_results);
278 if (!scope.ok()) return;
279 auto _skip = ::tensorflow::ops::AsNodeOut(scope, skip);
280 if (!scope.ok()) return;
281 ::tensorflow::Node* ret;
282 const auto unique_name = scope.GetUniqueNameForOp("SobolSample");
283 auto builder = ::tensorflow::NodeBuilder(unique_name, "SobolSample")
284 .Input(_dim)
285 .Input(_num_results)
286 .Input(_skip)
287 .Attr("dtype", attrs.dtype_)
288 ;
289 scope.UpdateBuilder(&builder);
290 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
291 if (!scope.ok()) return;
292 scope.UpdateStatus(scope.DoShapeInference(ret));
293 this->operation = Operation(ret);
294 this->samples = Output(ret, 0);
295}
296
297SobolSample::SobolSample(const ::tensorflow::Scope& scope, ::tensorflow::Input
298 dim, ::tensorflow::Input num_results,
299 ::tensorflow::Input skip)
300 : SobolSample(scope, dim, num_results, skip, SobolSample::Attrs()) {}
301
302SqrtGrad::SqrtGrad(const ::tensorflow::Scope& scope, ::tensorflow::Input y,
303 ::tensorflow::Input dy) {
304 if (!scope.ok()) return;
305 auto _y = ::tensorflow::ops::AsNodeOut(scope, y);
306 if (!scope.ok()) return;
307 auto _dy = ::tensorflow::ops::AsNodeOut(scope, dy);
308 if (!scope.ok()) return;
309 ::tensorflow::Node* ret;
310 const auto unique_name = scope.GetUniqueNameForOp("SqrtGrad");
311 auto builder = ::tensorflow::NodeBuilder(unique_name, "SqrtGrad")
312 .Input(_y)
313 .Input(_dy)
314 ;
315 scope.UpdateBuilder(&builder);
316 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
317 if (!scope.ok()) return;
318 scope.UpdateStatus(scope.DoShapeInference(ret));
319 this->operation = Operation(ret);
320 this->z = Output(ret, 0);
321}
322
323TanhGrad::TanhGrad(const ::tensorflow::Scope& scope, ::tensorflow::Input y,
324 ::tensorflow::Input dy) {
325 if (!scope.ok()) return;
326 auto _y = ::tensorflow::ops::AsNodeOut(scope, y);
327 if (!scope.ok()) return;
328 auto _dy = ::tensorflow::ops::AsNodeOut(scope, dy);
329 if (!scope.ok()) return;
330 ::tensorflow::Node* ret;
331 const auto unique_name = scope.GetUniqueNameForOp("TanhGrad");
332 auto builder = ::tensorflow::NodeBuilder(unique_name, "TanhGrad")
333 .Input(_y)
334 .Input(_dy)
335 ;
336 scope.UpdateBuilder(&builder);
337 scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));
338 if (!scope.ok()) return;
339 scope.UpdateStatus(scope.DoShapeInference(ret));
340 this->operation = Operation(ret);
341 this->z = Output(ret, 0);
342}
343
344} // namespace internal
345} // namespace ops
346} // namespace tensorflow
347