Searched refs:input_tuple (Results 1 – 7 of 7) sorted by relevance
| /external/tensorflow/tensorflow/compiler/xla/service/ |
| D | tuple_util.cc | 22 /*static*/ HloInstruction* TupleUtil::ExtractPrefix(HloInstruction* input_tuple, in ExtractPrefix() argument
24 CHECK(input_tuple->shape().IsTuple()); in ExtractPrefix()
26 HloComputation* computation = input_tuple->parent(); in ExtractPrefix()
27 const Shape& input_shape = input_tuple->shape(); in ExtractPrefix()
34 input_shape.tuple_shapes(i), input_tuple, i))); in ExtractPrefix()
42 HloInstruction* input_tuple, in AppendSuffix() argument
44 CHECK(input_tuple->shape().IsTuple()); in AppendSuffix()
46 HloComputation* computation = input_tuple->parent(); in AppendSuffix()
47 const Shape& input_shape = input_tuple->shape(); in AppendSuffix()
53 input_shape.tuple_shapes(i), input_tuple, i))); in AppendSuffix()
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| D | tuple_util.h | 30 static HloInstruction* ExtractPrefix(HloInstruction* input_tuple,
40 HloInstruction* input_tuple,
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| D | triangular_solve_expander.cc | 207 auto input_tuple = in InvertDiagonalBlocks() local
210 auto i = GetTupleElement(input_tuple, 0); in InvertDiagonalBlocks()
211 auto body_out = GetTupleElement(input_tuple, 1); in InvertDiagonalBlocks()
212 auto body_input = GetTupleElement(input_tuple, 2); in InvertDiagonalBlocks()
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| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| D | if_op.cc | 222 auto input_tuple = xla::Tuple(b, inputs); in Compile() local
224 xla::Conditional(ctx->Input(0), input_tuple, *then_result.computation, in Compile()
225 input_tuple, *else_result.computation); in Compile()
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| D | case_op.cc | 233 auto input_tuple = xla::Tuple(b, inputs); in Compile() local
237 std::vector<xla::XlaOp>(num_branches, input_tuple)); in Compile()
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | padding_fifo_queue_test.py | 1508 input_tuple = []
1518 input_tuple.append(np_array)
1520 q.enqueue_many(input_tuple).run()
1525 for (input_elem, output_elem) in zip(input_tuple, output_tuple):
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| D | fifo_queue_test.py | 1394 input_tuple = []
1404 input_tuple.append(np_array)
1406 q.enqueue_many(input_tuple).run()
1411 for (input_elem, output_elem) in zip(input_tuple, output_tuple):
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