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 _validate(self) -> None:
      super()._validate()
      values = self.values
      indices = self.indices
      assert indices.ndim > 1
      assert (-1 < indices.min()) & (indices.max() < len(values))
  
      event_shape = self.event_shape
      _event_shape = (
          indices.shape[-1] * values.shape[1],
      ) + values.shape[2:]
      if event_shape != _event_shape:
          raise ValueError(
              f"Shapes of `values` {values.shape} and `indices` "
              f"{indices.shape} incompatible with `event_shape` {event_shape}."
          )
  
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 6 for calling the SliceContainer obj's underlying member method code with example usage: obj._validate() without return types