class_docstr: The basic kernel class. Management of active dimensions is implemented here.
  
  :param active_dims: active dimensions, either a slice or list of
      indices into the columns of X.
  :param name: optional kernel name.
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docstr: Checks if the dimensions, over which the kernels are specified, overlap.
  Returns True if they are defined on different/separate dimensions and False otherwise.
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code: def on_separate_dims(self, other: "Kernel") -> bool:
      """
      Checks if the dimensions, over which the kernels are specified, overlap.
      Returns True if they are defined on different/separate dimensions and False otherwise.
      """
      if isinstance(self.active_dims, slice) or isinstance(
          other.active_dims, slice
      ):
          # Be very conservative for kernels defined over slices of dimensions
          return False
  
      if self.active_dims is None or other.active_dims is None:
          return False
  
      this_dims = self.active_dims.reshape(-1, 1)
      other_dims = other.active_dims.reshape(1, -1)
      return not np.any(this_dims == other_dims)
  
imports: import abc
  from functools import partial, reduce
  from typing import Callable, List, Optional, Sequence, Tuple, Union, overload
  import numpy
  import tensorflow
  from check_shapes import check_shapes
  from ..base import AnyNDArray, Module, TensorType
label_desc: Use image node_id 5 for calling the Kernel obj's underlying member method code with example usage: obj.on_separate_dims(other) and returns: unknown, False, False