code: def validate_inferred_shape(
      shard: torch.Tensor,
      head_size: int,
      n_local_q_heads: int,
      n_local_kv_heads: int,
  ):
      """
      Validate that the leading dim of the shard is of the expected size and aligns with the sharding
      logic for the attention computation itself.
      """
      inferred_leading_dim = head_size * (
          n_local_q_heads + 2 * n_local_kv_heads
      )
      assert shard.shape[0] == inferred_leading_dim
  
file_path: DeepSpeed/tests/unit/inference/v2/model_implementations/sharding/test_qkv_sharding.py
imports: from typing import Optional
  import pytest
  import torch
  from deepspeed.accelerator import get_accelerator
  from deepspeed.inference.v2.model_implementations.sharding import *
label_desc: Use image node_id 2 for calling a global function with example usage: validate_inferred_shape(shard, head_size, n_local_q_heads, n_local_kv_heads) without return types