parameters: (tik_instance, input_x, res, input_shape, shape_info)
return_statement: return tik_instance, res
docstring: input_shape is ((32, 16, 56, 56, 16), 'float16', (1, 1), (1, 1))
docstring_summary: input_shape is ((32, 16, 56, 56, 16), 'float16', (1, 1), (1, 1))
docstring_tokens: input_shape is (( 32 16 56 56 16 ) float16 ( 1 1 ) ( 1 1 ))
function: def shape56_3(tik_instance, input_x, res, input_shape, shape_info):
      """input_shape is ((32, 16, 56, 56, 16), 'float16', (1, 1), (1, 1))"""
      stride_w, stride_h, filter_w, filter_h, dilation_filter_w, dilation_filter_h = shape_info
      pad = [0, 0, 0, 0]
      l1_h, l1_w, c1_index, jump_stride, repeat_mode = 56, 56, 0, 1, 1
      with tik_instance.for_range(0, 32, block_num=32) as block_index:
          input_1_1_local_l1 = tik_instance.Tensor("float16", (25088 * 32 // 2,), scope=tik.scope_cbuf,
                                                   name="input_1_1_local_l1")
          input_1_1_fractal_l1_local_ub = tik_instance.Tensor("float16", (196 * 256 * 2,), scope=tik.scope_ubuf,
                                                              name="input_1_1_fractal_l1_local_ub")
          with tik_instance.for_range(0, 2) as eeb0, \
                  tik_instance.for_range(0, 4) as eeb1, \
                  tik_instance.for_range(0, 2) as eeb2:
              tik_instance.data_move(input_1_1_local_l1, input_x[block_index, eeb0 * 8, 0, 0, 0], 0, 1, 25088, 0, 0)
              fetch_filter_w, fetch_filter_h, left_top_h, left_top_w = 0, 0, 0, 0
              tik_instance.load3dv1(input_1_1_fractal_l1_local_ub[eeb2 * 196 * 256],
                                    input_1_1_local_l1[(eeb2 + eeb1 * 2) * 56 * 56 * 16],
                                    pad, l1_h, l1_w, c1_index, fetch_filter_w, fetch_filter_h,
                                    left_top_w, left_top_h, stride_w, stride_h, filter_w,
                                    filter_h, dilation_filter_w, dilation_filter_h,
                                    jump_stride, repeat_mode, 196)
              with tik_instance.for_range(0, 2) as eeb2, tik_instance.for_range(0, 196) as i:
                  tik_instance.data_move(res[eeb0 * 8 + eeb1 * 2 + eeb2, i + block_index * 196, 0, 0],
                                         input_1_1_fractal_l1_local_ub[256 * i + eeb2 * 196 * 256], 0, 1, 16,
                                         0, 0)
      return tik_instance, res
function_tokens: def shape56_3 ( tik_instance , input_x , res , input_shape , shape_info ) : stride_w , stride_h , filter_w , filter_h , dilation_filter_w , dilation_filter_h = shape_info pad = [ 0 , 0 , 0 , 0 ] l1_h , l1_w , c1_index , jump_stride , repeat_mode = 56 , 56 , 0 , 1 , 1 with tik_instance . for_range ( 0 , 32 , block_num = 32 ) as block_index : input_1_1_local_l1 = tik_instance . Tensor ( "float16" , ( 25088 * 32 // 2 , ) , scope = tik . scope_cbuf , name = "input_1_1_local_l1" ) input_1_1_fractal_l1_local_ub = tik_instance . Tensor ( "float16" , ( 196 * 256 * 2 , ) , scope = tik . scope_ubuf , name = "input_1_1_fractal_l1_local_ub" ) with tik_instance . for_range ( 0 , 2 ) as eeb0 , tik_instance . for_range ( 0 , 4 ) as eeb1 , tik_instance . for_range ( 0 , 2 ) as eeb2 : tik_instance . data_move ( input_1_1_local_l1 , input_x [ block_index , eeb0 * 8 , 0 , 0 , 0 ] , 0 , 1 , 25088 , 0 , 0 ) fetch_filter_w , fetch_filter_h , left_top_h , left_top_w = 0 , 0 , 0 , 0 tik_instance . load3dv1 ( input_1_1_fractal_l1_local_ub [ eeb2 * 196 * 256 ] , input_1_1_local_l1 [ ( eeb2 + eeb1 * 2 ) * 56 * 56 * 16 ] , pad , l1_h , l1_w , c1_index , fetch_filter_w , fetch_filter_h , left_top_w , left_top_h , stride_w , stride_h , filter_w , filter_h , dilation_filter_w , dilation_filter_h , jump_stride , repeat_mode , 196 ) with tik_instance . for_range ( 0 , 2 ) as eeb2 , tik_instance . for_range ( 0 , 196 ) as i : tik_instance . data_move ( res [ eeb0 * 8 + eeb1 * 2 + eeb2 , i + block_index * 196 , 0 , 0 ] , input_1_1_fractal_l1_local_ub [ 256 * i + eeb2 * 196 * 256 ] , 0 , 1 , 16 , 0 , 0 ) return tik_instance , res