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Searched refs:tensor_output (Results 1 – 8 of 8) sorted by relevance

/aosp_15_r20/external/executorch/backends/xnnpack/operators/
H A Dnode_visitor.py188 tensor_output = tensor
194 tensor_output = list(tensor.users)[0]
197 tensor_output in self.external_ids.keys()
200 ext_id = self.external_ids[tensor_output].external_id
202 flag = self.external_ids[tensor_output].io_type
/aosp_15_r20/external/pytorch/test/jit/
H A Dtest_dtype_analysis.py331 def assert_tensor_dtype_equal(self, tensor_output, graph_dtype): argument
332 if not isinstance(tensor_output, torch.Tensor):
334 self.assertEqual(tensor_output.dtype, graph_dtype)
/aosp_15_r20/external/tensorflow/tensorflow/python/kernel_tests/nn_ops/
H A Dfractional_avg_pool_op_test.py165 tensor_output, row_seq, col_seq = self.evaluate([p, r, c])
188 print(tensor_output[0, :, :, 0])
H A Dfractional_max_pool_op_test.py165 tensor_output, row_seq, col_seq = self.evaluate([p, r, c])
190 print(tensor_output[0, :, :, 0])
H A Dpooling_ops_test.py931 tensor_output = np.random.rand(*output_shape).astype(dtype)
936 grad_in = constant_op.constant(tensor_output, shape=output_shape)
945 grad_in = constant_op.constant(tensor_output, shape=output_shape)
/aosp_15_r20/external/pytorch/torch/csrc/jit/passes/quantization/
H A Dinsert_quant_dequant.cpp178 Value* tensor_output = tensor_node->output(); in insertScalarToTensor() local
179 tensor_output->setDebugName(scalar_value->debugName() + ".tensor"); in insertScalarToTensor()
182 scalar_value->replaceAllUsesAfterNodeWith(tensor_node, tensor_output); in insertScalarToTensor()
/aosp_15_r20/external/executorch/exir/emit/test/
H A Dtest_emit.py1162 tensor_output = torch.rand(1, 4)
1179 return tensor_output
1266 torch.allclose(executorch_module.run_method("get_tensor", [])[0], tensor_output)
/aosp_15_r20/external/pytorch/test/
H A Dtest_nn.py7003 tensor_output = flatten(tensor_input)
7004 self.assertEqual(tensor_output.size(), torch.Size([2, 6]))
7013 tensor_output = unflatten(tensor_input)
7014 self.assertEqual(tensor_output.size(), torch.Size([2, 2, 5, 5]))