class_docstr: Returns a tensor containing all windows of ``size``, separated by stride along the given ``axis``.
  
  Parameters
  ----------
  x: tensor<[*d0, d_axis, *dn], T>
      * Input tensor
  axis: const<i32>
      * Axis to perform the operation.
  size: const<i32>
      * Number of elements in the sliding window.
  stride: const<i32> Optional
      * Default to ``1``.
      * The stride of the input elements in the sliding window.
  
  Returns
  -------
  tensor<[*d0, d_axis - size // stride + 1, size, *dn], T>
      * The output will be a tensor of rank ``N+1`` where ``N`` is the input tensor rank.
  
  Attributes
  ----------
  T: fp32
code: def __init__(self, **kwargs):
      super(sliding_windows, self).__init__(**kwargs)
  
imports: import functools
  import sympy
  from coremltools.converters.mil.mil.types.symbolic import is_symbolic, isscalar, any_symbolic, any_variadic
  from coremltools.converters.mil.mil import get_new_symbol, get_new_variadic_symbol, VALUE, SYMBOL, types
  from ._op_reqs import *
label_desc: Use image node_id 1 to create a new sliding_windows object from inherited base classes: Operation with example: obj = sliding_windows()