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/packages/modules/NeuralNetworks/runtime/test/specs/V1_3/
Dbox_with_nms_limit_quant8_signed.mod.py22 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{18}") # scores out
23 o2 = Output("roiOut", "TENSOR_FLOAT32", "{18, 4}") # roi out
24 o3 = Output("classesOut", "TENSOR_INT32", "{18}") # classes out
25 o4 = Output("batchSplitOut", "TENSOR_INT32", "{18}") # batch split out
119 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{10}") # scores out
120 o2 = Output("roiOut", "TENSOR_FLOAT32", "{10, 4}") # roi out
121 o3 = Output("classesOut", "TENSOR_INT32", "{10}") # classes out
122 o4 = Output("batchSplitOut", "TENSOR_INT32", "{10}") # batch split out
208 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{12}") # scores out
209 o2 = Output("roiOut", "TENSOR_FLOAT32", "{12, 4}") # roi out
[all …]
Dconv2d_quant8_signed.mod.py23 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
45 o2 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 3, 1}")
74 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
96 o2 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 3, 1}")
126 o3 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 3, 1}")
153 o1 = Output("op4", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 3, 1, 3}, 1.f, 0")
168 o2 = Output("op4", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 3, 1, 3}, 1.f, 0")
182 o1 = Output("scoresOut", "TENSOR_QUANT8_ASYMM_SIGNED", "{0}, 0.1f, 0") # scores out
183 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
197 o3 = Output("out", "TENSOR_QUANT8_ASYMM_SIGNED", "{0, 2, 2, 3}, 1.f, 0") # out
[all …]
Dresize_quant8_signed.mod.py21 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 3, 1}")
45 o2 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 3, 2}")
69 o3 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 3, 1}")
96 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
97 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
108 o3 = Output("out", "TENSOR_FLOAT32", "{0, 3, 3, 1}") # out
134 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
135 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
146 o3 = Output("out", "TENSOR_FLOAT32", "{0, 3, 3, 1}") # out
171 o1 = Output("out", "TENSOR_FLOAT32", "{1, 1, 1, 1}") # output 0
[all …]
Dtranspose_quant8_signed.mod.py25 o1 = Output("op4", "TENSOR_FLOAT32", "{25, 32, 32, 16}") # output
59 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 5, 5, 2}") # output
108 o2 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 4, 1}") # output
141 o3 = Output("op4", "TENSOR_FLOAT32", "{1, 4, 4, 1}") # output
167 o4 = Output("op4", "TENSOR_FLOAT32", "{1, 6, 6, 1}") # output
194 o5 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 3, 1}") # output
219 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
220 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
234 o3 = Output("out", "TENSOR_FLOAT32", "{0, 5, 5, 2}") # out
262 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
[all …]
Dbidirectional_sequence_rnn_state_output.mod.py244 fw_output=Output(
247 bw_output=Output(
250 fw_output_hidden_state=Output(
253 bw_output_hidden_state=Output(
297 fw_output=Output(
300 bw_output=Output(
303 fw_output_hidden_state=Output(
306 bw_output_hidden_state=Output(
359 fw_output=Output(
364 fw_output_hidden_state=Output(
[all …]
Dsplit_quant8_signed.mod.py20 output0 = Output("output0", "TENSOR_QUANT8_ASYMM_SIGNED", "{2}, 1.0, -128")
21 output1 = Output("output1", "TENSOR_QUANT8_ASYMM_SIGNED", "{2}, 1.0, -128")
22 output2 = Output("output2", "TENSOR_QUANT8_ASYMM_SIGNED", "{2}, 1.0, -128")
43 output0 = Output("output0", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 3}, 2.0, -125")
44 output1 = Output("output1", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 3}, 2.0, -125")
64 output0 = Output("output0", "TENSOR_QUANT8_ASYMM_SIGNED", "{2, 1}, 2.0, -125")
65 output1 = Output("output1", "TENSOR_QUANT8_ASYMM_SIGNED", "{2, 1}, 2.0, -125")
66 output2 = Output("output2", "TENSOR_QUANT8_ASYMM_SIGNED", "{2, 1}, 2.0, -125")
87 output0 = Output("output0", "TENSOR_QUANT8_ASYMM_SIGNED",
89 output1 = Output("output1", "TENSOR_QUANT8_ASYMM_SIGNED",
Dresize_nearest_neighbor_v1_3.mod.py45 output0=Output("output0", "TENSOR_FLOAT32", "{1, 2, 2, 1}"),
58 output0=Output("output0", "TENSOR_FLOAT32", "{1, 1, 1, 1}"),
71 output0=Output("output0", "TENSOR_FLOAT32", "{1, 3, 3, 1}"),
84 output0=Output("output0", "TENSOR_FLOAT32", "{1, 2, 5, 1}"),
97 output0=Output("output0", "TENSOR_FLOAT32", "{1, 3, 3, 1}"),
110 output0=Output("output0", "TENSOR_FLOAT32", "{1, 5, 2, 1}"),
123 output0=Output("output0", "TENSOR_FLOAT32", "{1, 4, 4, 1}"),
136 output0=Output("output0", "TENSOR_FLOAT32", "{2, 3, 3, 2}"),
152 output0=Output("output0", "TENSOR_FLOAT32", "{1, 1, 1, 1}"),
165 output0=Output("output0", "TENSOR_FLOAT32", "{1, 3, 3, 1}"),
[all …]
Ddepthwise_conv2d_quant8_signed.mod.py23 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 4}")
51 o2 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 4}")
80 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 4}")
108 o2 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 4}")
137 o2 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 3, 1}")
169 o1 = Output("op4", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 1, 1, 2}, 1.f, -128")
186 o2 = Output("op4", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 2, 2, 4}, 1.f, 0")
206 o3 = Output("op4", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 2, 2, 4}, 1.f, 0")
226 output = Output("op4", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 2, 1, 4}, 1.f, -1")
258 output = Output("op4", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 1, 1, 2}, 1.f, -128")
[all …]
Dgather_quant8_signed.mod.py20 output0 = Output("output0", "TENSOR_FLOAT32", "{1, 3, 2, 2}")
63 output0=Output("output0", "TENSOR_FLOAT32", "{2, 2}"),
74 output0=Output("output0", "TENSOR_FLOAT32", "{1, 2}"),
84 output0=Output("output0", "TENSOR_FLOAT32", "{1}"),
93 output0=Output("output0", "TENSOR_FLOAT32", "{2}"),
102 output0=Output("output0", "TENSOR_FLOAT32", "{2, 2, 2}"),
115 output0=Output("output0", "TENSOR_FLOAT32", "{2, 1}"),
124 output0=Output("output0", "TENSOR_FLOAT32", "{1, 2, 3}"),
135 output0=Output("output0", "TENSOR_FLOAT32", "{1, 2, 2}"),
Davg_pool_quant8_signed.mod.py22 o = Output("op3", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 2, 2, 1}, 0.5f, -128")
57 output = Output("output", "TENSOR_QUANT8_ASYMM_SIGNED",
94 output = Output("output", "TENSOR_QUANT8_ASYMM_SIGNED",
116 o = Output("op3", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 3, 3, 1}, 0.5f, -128")
136 i3 = Output("op3", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 1, 2, 1}, 0.0625f, -128") # output 0
152 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
181 o2 = Output("op4", ("TENSOR_FLOAT32", [bat, output_row, output_col, chn]))
210 o3 = Output("op4", ("TENSOR_FLOAT32", [bat, output_row, output_col, chn]))
239 o4 = Output("op4", ("TENSOR_FLOAT32", [bat, output_row, output_col, chn]))
258 o5 = Output("op4", "TENSOR_FLOAT32", "{1, 1, 2, 1}")
[all …]
Dtranspose_conv2d_quant8_signed.mod.py25 o1 = Output("op4", "TENSOR_FLOAT32", "{25, 32, 32, 16}") # output
59 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 5, 5, 2}") # output
108 o2 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 4, 1}") # output
140 o3 = Output("op4", "TENSOR_FLOAT32", "{1, 4, 4, 1}") # output
166 o4 = Output("op4", "TENSOR_FLOAT32", "{1, 6, 6, 1}") # output
193 o5 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 3, 1}") # output
218 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
219 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
233 o3 = Output("out", "TENSOR_FLOAT32", "{0, 5, 5, 2}") # out
261 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
[all …]
Dmax_pool_quant8_signed.mod.py22 i3 = Output("op3", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 2, 2, 1}, 0.5f, -128") # output 0
54 output = Output("output", "TENSOR_QUANT8_ASYMM_SIGNED",
93 output = Output("output", "TENSOR_QUANT8_ASYMM_SIGNED",
118 i3 = Output("op3", "TENSOR_QUANT8_ASYMM_SIGNED", "{1, 1, 2, 1}, 0.0625f, -128") # output 0
134 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
163 o2 = Output("op4", ("TENSOR_FLOAT32", [bat, output_row, output_col, chn]))
192 o3 = Output("op4", ("TENSOR_FLOAT32", [bat, output_row, output_col, chn]))
211 o4 = Output("op4", "TENSOR_FLOAT32", "{1, 1, 2, 1}")
232 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
233 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
[all …]
Dcast_identity.mod.py29 as_output=Output("output0", "TENSOR_FLOAT16", "{2, 3}"),
36 as_output=Output("output0", "TENSOR_FLOAT32", "{2, 3}"),
43 as_output=Output("output0", "TENSOR_INT32", "{2, 3}"),
50 as_output=Output("output0", "TENSOR_BOOL8", "{2, 3}"),
55 as_output=Output("output0", "TENSOR_QUANT8_ASYMM", "{2, 3}, 4.0, 100"),
62 as_output=Output("output0", "TENSOR_QUANT8_SYMM", "{2, 3}, 4.0, 0"),
67 as_output=Output("output0", "TENSOR_QUANT16_ASYMM", "{2, 3}, 4.0, 100"),
72 as_output=Output("output0", "TENSOR_QUANT16_SYMM", "{2, 3}, 4.0, 0"),
/packages/modules/NeuralNetworks/runtime/test/specs/V1_2/
Ddetection_postprocess.mod.py22 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{1, 3}") # scores out
23 o2 = Output("roiOut", "TENSOR_FLOAT32", "{1, 3, 4}") # roi out
24 o3 = Output("classesOut", "TENSOR_INT32", "{1, 3}") # classes out
25 o4 = Output("detectionOut", "TENSOR_INT32", "{1}") # num detections out
73 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{1, 3}") # scores out
74 o2 = Output("roiOut", "TENSOR_FLOAT32", "{1, 3, 4}") # roi out
75 o3 = Output("classesOut", "TENSOR_INT32", "{1, 3}") # classes out
76 o4 = Output("detectionOut", "TENSOR_INT32", "{1}") # num detections out
124 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{1, 3}") # scores out
125 o2 = Output("roiOut", "TENSOR_FLOAT32", "{1, 3, 4}") # roi out
[all …]
Dtopk_v2.mod.py28 out_values=Output("out_values", "TENSOR_FLOAT32", "{2, 2}"),
30 out_indices=Output("out_indices", "TENSOR_INT32", "{2, 2}"),
36 out_values=Output("out_values", "TENSOR_FLOAT32", "{2, 2}"),
38 out_indices=Output("out_indices", "TENSOR_INT32", "{2, 2}"),
44 out_values=Output("out_values", "TENSOR_FLOAT32", "{2, 2}"),
46 out_indices=Output("out_indices", "TENSOR_INT32", "{2, 2}"),
52 out_values=Output("out_values", "TENSOR_FLOAT32", "{2}"),
54 out_indices=Output("out_indices", "TENSOR_INT32", "{2}"),
60 out_values=Output("out_values", "TENSOR_QUANT8_ASYMM", "{2, 2}, 2.0, 128"),
62 out_indices=Output("out_indices", "TENSOR_INT32", "{2, 2}"),
[all …]
Dslice.mod.py31 output=Output("output", "TENSOR_FLOAT32", "{2}"),
40 output=Output("output", "TENSOR_FLOAT32", "{1, 2}"),
49 output=Output("output", "TENSOR_FLOAT32", "{2, 3, 2}"),
58 output=Output("output", "TENSOR_FLOAT32", "{3, 1, 1, 1}"),
67 output=Output("output", "TENSOR_INT32", "{1, 1, 3, 1}"),
76 output=Output("output", "TENSOR_INT32", "{2, 1, 3, 1}"),
85 output=Output("output", "TENSOR_QUANT8_ASYMM", "{2, 1, 3, 1}, 2.0, 128"),
94 output=Output("output", "TENSOR_INT32", "{2, 1, 3, 1}"),
115 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
116 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
[all …]
Ddequantize_v1_2.mod.py31 output0=Output("output0", "TENSOR_FLOAT32", "{10}"),
39 output0=Output("output0", "TENSOR_FLOAT32", "{2, 5}"),
47 output0=Output("output0", "TENSOR_FLOAT32", "{2, 2, 2}"),
55 output0=Output("output0", "TENSOR_FLOAT32", "{2, 1, 2, 2}"),
65 output0=Output("output0", "TENSOR_FLOAT32", "{2, 3, 4}"),
82 output0=Output("output0", "TENSOR_FLOAT32", "{2, 3, 4}"),
104 i2 = Output("op2", "TENSOR_FLOAT16", "{1, 2, 2, 1}")
123 o1 = Output("scoresOut", "TENSOR_QUANT8_ASYMM", "{0}, 0.1f, 128") # scores out
124 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
136 o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 1}") # out
Dconv2d_v1_2.mod.py23 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
51 o2 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 4, 1}")
79 o3 = Output("op4", "TENSOR_FLOAT32", "{1, 1, 1, 3}")
107 o4 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 3, 3}")
147 o5 = Output("op4", "TENSOR_FLOAT32", "{1, 8, 8, 1}")
148 o6 = Output("op4", "TENSOR_FLOAT32", "{1, 6, 7, 1}")
173 o7 = Output("op4", "TENSOR_FLOAT32", "{1, 8, 8, 3}")
174 o8 = Output("op4", "TENSOR_FLOAT32", "{1, 6, 7, 3}")
201 o9 = Output("op4", ("TENSOR_QUANT8_ASYMM", [2, 1, 2, 3], 1.0, 127))
219 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
[all …]
Dresize_nearest_neighbor.mod.py21 o1 = Output("out", "TENSOR_FLOAT32", "{1, 1, 1, 1}") # output 0
42 o1 = Output("out", "TENSOR_FLOAT32", "{1, 3, 3, 1}") # output 0
63 o1 = Output("out", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # output 0
84 o1 = Output("out", "TENSOR_FLOAT32", "{1, 2, 5, 1}") # output 0
105 o1 = Output("out", "TENSOR_FLOAT32", "{1, 3, 3, 1}") # output 0
126 o1 = Output("out", "TENSOR_FLOAT32", "{1, 5, 2, 1}") # output 0
147 o1 = Output("out", "TENSOR_FLOAT32", "{1, 4, 4, 1}") # output 0
168 o1 = Output("out", "TENSOR_FLOAT32", "{2, 3, 3, 2}") # output 0
194 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
195 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
[all …]
Dbox_with_nms_limit_hard.mod.py22 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{12}") # scores out
23 o2 = Output("roiOut", "TENSOR_FLOAT32", "{12, 4}") # roi out
24 o3 = Output("classesOut", "TENSOR_INT32", "{12}") # classes out
25 o4 = Output("batchSplitOut", "TENSOR_INT32", "{12}") # batch split out
109 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{10}") # scores out
110 o2 = Output("roiOut", "TENSOR_FLOAT32", "{10, 4}") # roi out
111 o3 = Output("classesOut", "TENSOR_INT32", "{10}") # classes out
112 o4 = Output("batchSplitOut", "TENSOR_INT32", "{10}") # batch split out
Dbox_with_nms_limit_linear.mod.py22 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{16}") # scores out
23 o2 = Output("roiOut", "TENSOR_FLOAT32", "{16, 4}") # roi out
24 o3 = Output("classesOut", "TENSOR_INT32", "{16}") # classes out
25 o4 = Output("batchSplitOut", "TENSOR_INT32", "{16}") # batch split out
116 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{15}") # scores out
117 o2 = Output("roiOut", "TENSOR_FLOAT32", "{15, 4}") # roi out
118 o3 = Output("classesOut", "TENSOR_INT32", "{15}") # classes out
119 o4 = Output("batchSplitOut", "TENSOR_INT32", "{15}") # batch split out
Dbox_with_nms_limit_gaussian.mod.py22 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{18}") # scores out
23 o2 = Output("roiOut", "TENSOR_FLOAT32", "{18, 4}") # roi out
24 o3 = Output("classesOut", "TENSOR_INT32", "{18}") # classes out
25 o4 = Output("batchSplitOut", "TENSOR_INT32", "{18}") # batch split out
118 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{10}") # scores out
119 o2 = Output("roiOut", "TENSOR_FLOAT32", "{10, 4}") # roi out
120 o3 = Output("classesOut", "TENSOR_INT32", "{10}") # classes out
121 o4 = Output("batchSplitOut", "TENSOR_INT32", "{10}") # batch split out
Dl2_pool_v1_2.mod.py21 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
33 o2 = Output("op4", "TENSOR_FLOAT32", "{1, 1, 2, 1}")
45 o3 = Output("op4", "TENSOR_FLOAT32", "{1, 1, 1, 3}")
60 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
61 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
72 o3 = Output("out", "TENSOR_FLOAT32", "{0, 1, 1, 1}") # out
88 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
89 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
100 o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 1}") # out
Dtranspose_conv2d.mod.py25 o1 = Output("op4", "TENSOR_FLOAT32", "{1, 5, 5, 2}") # output
73 o2 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 4, 1}") # output
105 o3 = Output("op4", "TENSOR_FLOAT32", "{1, 4, 4, 1}") # output
131 o4 = Output("op4", "TENSOR_FLOAT32", "{1, 6, 6, 1}") # output
158 o5 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 3, 1}") # output
183 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
184 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
198 o3 = Output("out", "TENSOR_FLOAT32", "{0, 5, 5, 2}") # out
226 o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
227 o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
[all …]
/packages/apps/Messaging/src/com/android/messaging/util/
DFallbackStrategies.java49 public class FallbackStrategies<Input, Output> {
50 public interface Strategy<Input, Output> {
51 Output execute(Input params) throws Exception; in execute()
54 private final List<Strategy<Input, Output>> mChainedStrategies;
56 private FallbackStrategies(final Strategy<Input, Output> primaryStrategy) { in FallbackStrategies()
57 mChainedStrategies = new ArrayList<Strategy<Input, Output>>(); in FallbackStrategies()
61 public static <Input, Output> FallbackStrategies<Input, Output> startWith( in startWith()
62 final Strategy<Input, Output> primaryStrategy) { in startWith()
63 return new FallbackStrategies<Input, Output>(primaryStrategy); in startWith()
66 public FallbackStrategies<Input, Output> thenTry(final Strategy<Input, Output> strategy) { in thenTry()
[all …]

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