def create_test_input(batch_size, height, width, channels):
 if (None in [batch_size, height, width, channels]):
  return tf.placeholder(tf.float32, (batch_size, height, width, channels))
 else:
  return tf.to_float(np.tile(np.reshape((np.reshape(np.arange(height), [height, 1]) + np.reshape(np.arange(width), [1, width])), [1, height, width, 1]), [batch_size, 1, 1, channels]))

def create_test_input ( batch_size , height , width , channels ) : if ( None in [ batch_size , height , width , channels ] ) : return tf . placeholder ( tf . float32 , ( batch_size , height , width , channels ) ) else : return tf . to_float ( np . tile ( np . reshape ( ( np . reshape ( np . arange ( height ) , [ height , 1 ] ) + np . reshape ( np . arange ( width ) , [ 1 , width ] ) ) , [ 1 , height , width , 1 ] ) , [ batch_size , 1 , 1 , channels ] ) )
create test input tensor .