code: def crop_and_resize(
      data,
      boxes,
      box_indices,
      crop_size,
      layout,
      method="bilinear",
      extrapolation_value=0,
      out_dtype=None,
  ):
      """Crop input images and resize them.
  
      method indicates the algorithm to be used while calculating the out value
      and method can be either "bilinear" or "nearest_neighbor".
  
      Parameters
      ----------
      data : relay.Expr
          The input data to the operator.
  
      boxes : relay.Expr
          A 2-D tensor of shape [num_boxes, 4]. Each row of the tensor specifies
          the coordinates of a box.
  
      box_indices : relay.Expr
          A 1-D tensor of shape [num_boxes], box_ind[i] specifies the data that
          the i-th box refers to.
  
      crop_size : Tuple of PrimExpr
          The target size to which each box will be resized.
  
      layout : str, optional
          Layout of the input.
  
      method : str, optional
          Scale method, it can be either "nearest_neighbor" or "bilinear".
  
      extrapolation_value : float, optional
          Value used for extrapolation, when applicable.
  
      out_dtype : str, optional
          Type to return. If left None returns the same type as input.
  
      Returns
      -------
      result: relay.Expr
          The computed result.
      """
      return _make.crop_and_resize(
          data,
          boxes,
          box_indices,
          crop_size,
          layout,
          method,
          extrapolation_value,
          out_dtype,
      )
  
file_path: tvm/python/tvm/relay/op/image/image.py
imports: from .None import _make
  from ..dyn.image import _make
  from ...expr import Expr, Constant, const
label_desc: Use image node_id 4 for calling a global function with example usage: crop_and_resize(data, boxes, box_indices, crop_size, layout, method, extrapolation_value, out_dtype) and returns: _make