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 __call__(self) -> Tensor:
      flat = self.values.index_select(
          dim=0, index=self.indices.view(-1)
      )
      return flat.view(
          *self.indices.shape[:-1], -1, *self.values.shape[2:]
      )
  
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 1 for calling the SliceContainer obj's underlying member method code with example usage: obj.__call__() and returns: flat