code: def sharded_embedding_dim(
      embedding_size: int, shard_rank: int, num_shards: int
  ) -> int:
      """
      Utility method for getting the size of the embedding dimension of a sharded embedding.
  
      Args:
          embedding_size (int): The size of the embedding.
          shard_rank (int): Which shard of the partitioned tensor to return.
          num_shards (int): The total number of shards the parameter is distributed across.
      """
      start_idx, end_idx = get_shard_endpoints(
          embedding_size, shard_rank, num_shards
      )
      return end_idx - start_idx
  
imports: import torch
  from .types import ShardingType
  from .utils import shard_param, get_shard_endpoints
label_desc: Use image node_id 2 for calling a global function with example usage: sharded_embedding_dim(embedding_size, shard_rank, num_shards) and returns: unknown