//  [python]
def bilinear_sampler(imgs, coords):
    """Construct a new image by bilinear sampling from the input image.

    Points falling outside the source image boundary have value 0.

    Args:
      imgs: source image to be sampled from [batch, height_s, width_s, channels]
      coords: coordinates of source pixels to sample from [batch, height_t,
        width_t, 2]. height_t/width_t correspond to the dimensions of the output
        image (don't need to be the same as height_s/width_s). The two channels
        correspond to x and y coordinates respectively.
    Returns:
      A new sampled image [batch, height_t, width_t, channels]
    """

    with tf.name_scope('image_sampling'):
        coords_x, coords_y = tf.split(coords, [1, 1], axis=-1)
        inp_size = imgs.get_shape()
        coord_size = coords.get_shape()
        out_size = coords.get_shape().as_list()
        out_size[3] = imgs.get_shape().as_list()[3]

        coords_x = tf.to_float(coords_x)
        coords_y = tf.to_float(coords_y)

        x0 = tf.floor(coords_x)
        x1 = x0 + 1
        y0 = tf.floor(coords_y)
        y1 = y0 + 1

        y_max = tf.cast(tf.shape(imgs)[1] - 1, 'float32')
        x_max = tf.cast(tf.shape(imgs)[2] - 1, 'float32')
        zero = tf.zeros([1], dtype='float32')

        x0_safe = tf.clip_by_value(x0, zero, x_max)
        y0_safe = tf.clip_by_value(y0, zero, y_max)
        x1_safe = tf.clip_by_value(x1, zero, x_max)
        y1_safe = tf.clip_by_value(y1, zero, y_max)

        ## bilinear interp weights, with points outside the grid having weight 0
        """
        weight of point take the value of far distance
        """
        wt_x0 = x1_safe - coords_x
        wt_x1 = coords_x - x0_safe
        wt_y0 = y1_safe - coords_y
        wt_y1 = coords_y - y0_safe

        ## indices in the flat image to sample from
        dim2 = tf.cast(inp_size[2], 'float32')
        dim1 = tf.cast(inp_size[2] * inp_size[1], 'float32')
        base = _repeat(tf.cast(tf.range(coord_size[0]), 'float32') * dim1, coord_size[1] * coord_size[2])
        base = tf.reshape(base, [out_size[0], out_size[1], out_size[2], 1])

        base_y0 = base + y0_safe * dim2
        base_y1 = base + y1_safe * dim2

        """
        idx00 mean the index of image_flat that int(lt of current coordinate)
        """
        idx00 = tf.reshape(x0_safe + base_y0, [-1])
        idx01 = x0_safe + base_y1
        idx10 = x1_safe + base_y0
        idx11 = x1_safe + base_y1

        ## sample from imgs
        imgs_flat = tf.reshape(imgs, tf.stack([-1, inp_size[3]]))
        imgs_flat = tf.to_float(imgs_flat)
        im00 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx00, 'int32')), out_size)
        im01 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx01, 'int32')), out_size)
        im10 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx10, 'int32')), out_size)
        im11 = tf.reshape(tf.gather(imgs_flat, tf.cast(idx11, 'int32')), out_size)

        w00 = wt_x0 * wt_y0
        w01 = wt_x0 * wt_y1
        w10 = wt_x1 * wt_y0
        w11 = wt_x1 * wt_y1

        output = tf.add_n([w00 * im00, w01 * im01, w10 * im10, w11 * im11])
        mask = tf.add_n([w00, w01, w10, w11])

        return output, mask