code: def vector_scalar_to_grid(
      src_crs, target_proj, regrid_shape, x, y, u, v, *scalars, **kwargs
  ):
      """
      Transform and interpolate a vector field to a regular grid in the
      target projection.
  
      Parameters
      ----------
      src_crs
          The :class:`~cartopy.crs.CRS` that represents the coordinate
          system the vectors are defined in.
      target_proj
          The :class:`~cartopy.crs.Projection` that represents the
          projection the vectors are to be transformed to.
      regrid_shape
          The regular grid dimensions. If a single integer then the grid
          will have that number of points in the x and y directions. A
          2-tuple of integers specify the size of the regular grid in the
          x and y directions respectively.
      x, y
          The x and y coordinates, in the source CRS coordinates,
          where the vector components are located.
      u, v
          The grid eastward and grid northward components of the
          vector field respectively. Their shapes must match.
  
      Other Parameters
      ----------------
      scalars
          Zero or more scalar fields to regrid along with the vector
          components. Each scalar field must have the same shape as the
          vector components.
      target_extent
          The extent in the target CRS that the grid should occupy, in the
          form ``(x-lower, x-upper, y-lower, y-upper)``. Defaults to cover
          the full extent of the vector field.
  
      Returns
      -------
      x_grid, y_grid
          The x and y coordinates of the regular grid points as
          2-dimensional arrays.
      u_grid, v_grid
          The eastward and northward components of the vector field on
          the regular grid.
      scalars_grid
          The scalar fields on the regular grid. The number of returned
          scalar fields is the same as the number that were passed in.
  
      """
      x = np.asanyarray(x)
      y = np.asanyarray(y)
      u = np.asanyarray(u)
      v = np.asanyarray(v)
  
      if u.shape != v.shape:
          raise ValueError("u and v must be the same shape")
      if x.shape != u.shape:
          x, y = np.meshgrid(x, y)
          if not (x.shape == y.shape == u.shape):
              raise ValueError(
                  "x and y coordinates are not compatible "
                  "with the shape of the vector components"
              )
      if scalars:
          np_like_scalars = ()
          for s in scalars:
              s = np.asanyarray(s)
              np_like_scalars = np_like_scalars + (s,)
              if s.shape != u.shape:
                  raise ValueError(
                      "scalar fields must have the same "
                      "shape as the vector components"
                  )
          scalars = np_like_scalars
      try:
          nx, ny = regrid_shape
      except TypeError:
          nx = ny = regrid_shape
      if target_proj == src_crs:
          # Just immediately regrid, interpolate and return
          return _interpolate_to_grid(
              nx, ny, x, y, u, v, *scalars, **kwargs
          )
  
      # We need to transform the vectors from the source to target frame
      # Convert coordinates to the target projection.
      proj_xyz = target_proj.transform_points(src_crs, x, y)
      targetx, targety = proj_xyz[..., 0], proj_xyz[..., 1]
  
      # Create the grid in the target frame
      gridx, gridy = _interpolate_to_grid(
          nx, ny, targetx, targety, **kwargs
      )
  
      # Bring the x/y target grid coordinates back into the source frame
      src_xyz = src_crs.transform_points(target_proj, gridx, gridy)
      # Mask the invalid points that were outside the domain
      src_xyz = np.ma.array(src_xyz, mask=~np.isfinite(src_xyz))
      sourcex, sourcey = src_xyz[..., 0], src_xyz[..., 1]
  
      # Now interpolate in the source frame
      x0, x1 = sourcex.min(), sourcex.max()
      y0, y1 = sourcey.min(), sourcey.max()
      xr = x1 - x0
      yr = y1 - y0
      # We also need to transform the original source points to the source
      # projection to account for original points outside the wrapped domain
      xyz = src_crs.transform_points(src_crs, x, y)
      x, y = xyz[..., 0], xyz[..., 1]
      points = np.column_stack(
          [(x.ravel() - x0) / xr, (y.ravel() - y0) / yr]
      )
      newx = (sourcex - x0) / xr
      newy = (sourcey - y0) / yr
      s_grid_tuple = tuple()
      for s in (u, v) + scalars:
          s_grid_tuple += (
              griddata(
                  points, s.ravel(), (newx, newy), method="linear"
              ),
          )
  
      u, v = s_grid_tuple[0], s_grid_tuple[1]
      # Finally, transform the vectors (in the source frame) to the target CRS.
      u, v = target_proj.transform_vectors(
          src_crs, sourcex, sourcey, u, v
      )
  
      return (gridx, gridy, u, v) + s_grid_tuple[2:]
  
label_desc: Use image node_id 2 for calling a global function with example usage: vector_scalar_to_grid(src_crs, target_proj, regrid_shape, x, y, u, v) and returns: unknown, _interpolate_to_grid