class_docstr: Represent data points formed by concatenating (n-1)-dimensional slices
  taken from the leading dimension of an n-dimensional source tensor.
class_docstr_tok: ["Represent","data","points","formed","by","concatenating","(","n-1",")","-dimensional","slices","taken","from","the","leading","dimension","of","an","n-dimensional","source","tensor","."]
code: def __eq__(self, other: Any) -> bool:
      return (
          type(other) is type(self)
          and self.values.equal(other.values)
          and self.indices.equal(other.indices)
      )
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from dataclasses import dataclass, fields
  from typing import Any
  from torch import device, dtype, LongTensor, Size, Tensor
label_desc: Use image node_id 2 for calling the SliceContainer obj's underlying member method code with example usage: obj.__eq__(other) and returns: unknown