code: def solve_slice_by_index_slice(
      x_shape, begin, end, stride, begin_mask, end_mask, squeeze_mask
  ):
      """
      Utility function to solve the slices of tensor slicing
      """
      # set default values for parameters
      rank = len(x_shape)
      begin = [int(i) for i in list(begin[:])]
      end = [int(i) for i in list(end[:])]
      if stride is None:
          stride = [1] * rank
      if begin_mask is None:
          begin_mask = [False] * rank
      if end_mask is None:
          end_mask = [False] * rank
      if squeeze_mask is None:
          squeeze_mask = [False] * rank
  
      # compute slices
      slices = []
      for idx, mask in enumerate(begin_mask):
          if mask:
              begin[idx] = None
      for idx, mask in enumerate(end_mask):
          if mask:
              end[idx] = None
      for idx, mask in enumerate(squeeze_mask):
          if mask:
              end[idx] = None
              stride[idx] = np.iinfo(
                  np.int32
              ).max  # We slice out only 1 element by setting stride to INF
      for idx in range(rank):
          slices.append(slice(begin[idx], end[idx], stride[idx]))
  
      return tuple(slices)
  
imports: import math
  import numbers
  from typing import List, Tuple
  import numpy
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Var, get_new_symbol, types
  from coremltools.converters.mil.mil.ops.defs.iOS15.elementwise_unary import cast
  from coremltools.converters.mil.mil.types import builtin_to_string, promote_dtypes
  from coremltools.converters.mil.mil.types.symbolic import is_symbolic
label_desc: Use image node_id 12 for calling a global function with example usage: solve_slice_by_index_slice(x_shape, begin, end, stride, begin_mask, end_mask, squeeze_mask) and returns: tuple