code: def _check_support(indexes):
      if all(
          (
              isinstance(ix, (TENSOR_TYPE, np.ndarray))
              and ix.dtype != np.bool_
              or isinstance(ix, slice)
              and ix == slice(None)
          )
          for ix in indexes
      ):
          if any(
              isinstance(ix, (TENSOR_TYPE, np.ndarray))
              for ix in indexes
          ):
              return True
      for index in indexes:
          if isinstance(index, (slice, Integral)):
              pass
          elif (
              isinstance(index, (np.ndarray, TENSOR_TYPE))
              and index.dtype == np.bool_
          ):
              pass
          else:  # pragma: no cover
              raise NotImplementedError(
                  "Only slice, int, or bool indexing "
                  f"supported by now, got {type(index)}"
              )
      return False
  
imports: import functools
  import itertools
  import operator
  from numbers import Integral
  from typing import Union
  import numpy
  from ...None import opcodes
  from ...core import ENTITY_TYPE, recursive_tile
  from ...core.context import Context
  from ...core.operand import OperandStage
  from ...serialization.serializables import KeyField, TupleField, AnyField, BoolField
  from ...tensor import tensor
  from ...utils import has_unknown_shape
  from ..base import broadcast_to
  from ..core import TENSOR_TYPE, TensorOrder
  from ..operands import TensorMapReduceOperand, TensorOperandMixin, TensorShuffleProxy
  from ..utils import broadcast_shape, filter_inputs
  from .core import process_index
label_desc: Use image node_id 1 for calling a global function with example usage: _check_support(indexes) and returns: False, True