parameters: (self, conv_layer_count, weight_shape, matrix_a_inv)
return_statement: return matrix_a_inv
docstring: process conv matmul device pad
docstring_summary: process conv matmul device pad
docstring_tokens: process conv matmul device pad
function: def _process_conv_matmul_device_pad(self, conv_layer_count, weight_shape, matrix_a_inv):
          """process conv matmul device pad"""
          if self.device_shape_pad_flag[conv_layer_count]:
              kernel_hw = weight_shape[2] * weight_shape[3]
              in_channels = weight_shape[1]
              matrix_a_inv = self.reshape(matrix_a_inv, (kernel_hw, in_channels, kernel_hw, in_channels))
              matrix_a_inv = P.Pad(((0, 0), (0, self.C0 - in_channels), (0, 0),
                                    (0, self.C0 - in_channels)))(matrix_a_inv)
          return matrix_a_inv
function_tokens: def _process_conv_matmul_device_pad ( self , conv_layer_count , weight_shape , matrix_a_inv ) : if self . device_shape_pad_flag [ conv_layer_count ] : kernel_hw = weight_shape [ 2 ] * weight_shape [ 3 ] in_channels = weight_shape [ 1 ] matrix_a_inv = self . reshape ( matrix_a_inv , ( kernel_hw , in_channels , kernel_hw , in_channels ) ) matrix_a_inv = P . Pad ( ( ( 0 , 0 ) , ( 0 , self . C0 - in_channels ) , ( 0 , 0 ) , ( 0 , self . C0 - in_channels ) ) ) ( matrix_a_inv ) return matrix_a_inv