code: def mask(
      dataset,
      shapes,
      all_touched=False,
      invert=False,
      nodata=None,
      filled=True,
      crop=False,
      pad=False,
      pad_width=0.5,
      indexes=None,
  ):
      """Creates a masked or filled array using input shapes.
      Pixels are masked or set to nodata outside the input shapes, unless
      `invert` is `True`.
  
      If shapes do not overlap the raster and crop=True, a ValueError is
      raised.  Otherwise, a warning is raised.
  
      Parameters
      ----------
      dataset : a dataset object opened in 'r' mode
          Raster to which the mask will be applied.
      shapes : iterable object
          The values must be a GeoJSON-like dict or an object that implements
          the Python geo interface protocol (such as a Shapely Polygon).
      all_touched : bool (opt)
          Include a pixel in the mask if it touches any of the shapes.
          If False (default), include a pixel only if its center is within one of
          the shapes, or if it is selected by Bresenham's line algorithm.
      invert : bool (opt)
          If False (default) pixels outside shapes will be masked.  If True,
          pixels inside shape will be masked.
      nodata : int or float (opt)
          Value representing nodata within each raster band. If not set,
          defaults to the nodata value for the input raster. If there is no
          set nodata value for the raster, it defaults to 0.
      filled : bool (opt)
          If True, the pixels outside the features will be set to nodata.
          If False, the output array will contain the original pixel data,
          and only the mask will be based on shapes.  Defaults to True.
      crop : bool (opt)
          Whether to crop the raster to the extent of the shapes. Defaults to
          False.
      pad : bool (opt)
          If True, the features will be padded in each direction by
          one half of a pixel prior to cropping raster. Defaults to False.
      indexes : list of ints or a single int (opt)
          If `indexes` is a list, the result is a 3D array, but is
          a 2D array if it is a band index number.
  
      Returns
      -------
      tuple
  
          Two elements:
  
              masked : numpy.ndarray or numpy.ma.MaskedArray
                  Data contained in the raster after applying the mask. If
                  `filled` is `True` and `invert` is `False`, the return will be
                  an array where pixels outside shapes are set to the nodata value
                  (or nodata inside shapes if `invert` is `True`).
  
                  If `filled` is `False`, the return will be a MaskedArray in
                  which pixels outside shapes are `True` (or `False` if `invert`
                  is `True`).
  
              out_transform : affine.Affine()
                  Information for mapping pixel coordinates in `masked` to another
                  coordinate system.
      """
  
      if nodata is None:
          if dataset.nodata is not None:
              nodata = dataset.nodata
          else:
              nodata = 0
  
      shape_mask, transform, window = raster_geometry_mask(
          dataset,
          shapes,
          all_touched=all_touched,
          invert=invert,
          crop=crop,
          pad=pad,
          pad_width=pad_width,
      )
  
      if indexes is None:
          out_shape = (dataset.count,) + shape_mask.shape
      elif isinstance(indexes, int):
          out_shape = shape_mask.shape
      else:
          out_shape = (len(indexes),) + shape_mask.shape
  
      out_image = dataset.read(
          window=window,
          out_shape=out_shape,
          masked=True,
          indexes=indexes,
      )
  
      out_image.mask = out_image.mask | shape_mask
  
      if filled:
          out_image = out_image.filled(nodata)
  
      return out_image, transform
  
imports: import logging
  import warnings
  import numpy
  from rasterio.errors import WindowError
  from rasterio.features import geometry_mask, geometry_window
label_desc: Use image node_id 2 for calling a global function with example usage: mask(dataset, shapes, all_touched, invert, nodata, filled, crop, pad, pad_width, indexes) and returns: out_image,  transform