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/external/libaom/libaom/aom_dsp/
Dnoise_model.c32 int stride, int x_o, int y_o, \
35 const int max_w = AOMMIN(w - x_o, block_size); \
39 block_mean += data[(y_o + y) * stride + x_o + x]; \
49 int stride, int x_o, int y_o, in get_block_mean() argument
52 return get_block_mean_highbd((const uint16_t *)data, w, h, stride, x_o, y_o, in get_block_mean()
54 return get_block_mean_lowbd(data, w, h, stride, x_o, y_o, block_size); in get_block_mean()
62 int h, int x_o, int y_o, int block_size_x, int block_size_y) { \
64 const int max_w = AOMMIN(w - x_o, block_size_x); \
69 double noise = (double)data[(y_o + y) * stride + x_o + x] - \
70 denoised[(y_o + y) * stride + x_o + x]; \
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/external/tensorflow/tensorflow/python/keras/layers/
Drecurrent.py2397 x_i, x_f, x_c, x_o = x
2406 x_o + K.dot(h_tm1_o, self.recurrent_kernel[:, self.units * 3:]))
2442 x_o = K.dot(inputs_o, k_o)
2449 x_o = K.bias_add(x_o, b_o)
2461 x = (x_i, x_f, x_c, x_o)
2617 x_i, x_f, x_c, x_o = x
2628 x_o + K.dot(h_tm1_o, self.recurrent_kernel[:, self.units * 3:]) +
Dconvolutional_recurrent.py637 x_o = self.input_conv(inputs_o, kernel_o, bias_o, padding=self.padding)
646 o = self.recurrent_activation(x_o + h_o)
/external/toolchain-utils/android_bench_suite/panorama_input/
Dtest_008.ppm7897 ����# %33?.;H6ES>LZEP^FZhQ`nVhv^m}gm}gaq[izd{�wz�vpou�tpoXgWapa_n_l{h|�x_o]]mZThW6J8I]L>…