code: def _reshape_mask(
      view: View, new_shape: Tuple[sint, ...]
  ) -> Tuple[Optional[Tuple[Tuple[sint, sint], ...]], bool]:
      if view.mask is None:
          return view.mask, False
      if any(
          not isinstance(m[0], int) or not isinstance(m[1], int)
          for m in view.mask
      ):
          return view.mask, True
      new_mask: List[Tuple[int, int]] = []
  
      r_masks, r_shape, r_new_shape = (
          reversed(view.mask),
          reversed(view.shape),
          reversed(new_shape),
      )
      curr_stride, old_dim, new_dim, mask = (
          1,
          next(r_shape, 1),
          next(r_new_shape, 1),
          next(r_masks, (0, 1)),
      )
      if mask[1] - mask[0] < 1:
          return ((0, 0),) * len(new_shape), False  # invalid mask
  
      while len(new_mask) < len(new_shape):
          (l, r), next_stride = mask, new_dim * curr_stride
  
          if old_dim >= next_stride:  # need to split mask.
              if (
                  old_dim == next_stride
              ):  # simply copy the mask and get next batch for merging
                  new_mask.append(
                      (l // curr_stride, (r - 1) // curr_stride + 1)
                  )
                  curr_stride, old_dim, new_dim, mask = (
                      1,
                      next(r_shape, 1),
                      next(r_new_shape, 1),
                      next(r_masks, (0, 1)),
                  )
                  if mask[1] - mask[0] < 1:
                      return ((0, 0),) * len(
                          new_shape
                      ), False  # invalid mask
  
              else:  # mask can only be splitted if reshape doesn't cut across the mask.
                  if (
                      l % next_stride != 0 or r % next_stride != 0
                  ) and l // next_stride != (r - 1) // next_stride:
                      return view.mask, True
                  new_mask.append(
                      (
                          l % next_stride // curr_stride,
                          (r - 1) % next_stride // curr_stride + 1,
                      )
                  )
                  curr_stride, new_dim = next_stride, next(
                      r_new_shape, 1
                  )  # need to get mask for next dimension
  
          else:
              next_mask = next(r_masks, (0, 1))
              # combine if the mask can unfold continuously
              if (
                  mask != (0, old_dim)
                  and next_mask[1] - next_mask[0] != 1
              ):
                  return view.mask, True
              mask, old_dim = (
                  next_mask[0] * old_dim + l,
                  (next_mask[1] - 1) * old_dim + r,
              ), old_dim * next(r_shape, 1)
  
      for (
          mask
      ) in (
          r_masks
      ):  # if the old shape has leading 1s, need to make sure their mask is (0,1)
          if mask != (0, 1):
              return ((0, 0),) * len(new_shape), False  # invalid mask
  
      return tuple(reversed(new_mask)), False
  
imports: from __future__ import annotations
  import functools, operator, itertools
  from dataclasses import dataclass
  from typing import Tuple, List, Optional, Dict, cast
  from tinygrad.helpers import prod, all_int, argsort
  from tinygrad.shape.symbolic import Node, NumNode, Variable, Set, sint
label_desc: Use image node_id 4 for calling a global function with example usage: _reshape_mask(view, new_shape) and returns: tuple,  False, view,  False, view,  True, unknown,  False, unknown,  False, view,  True, unknown,  False, view,  True