code: def get_matmul_type(input_dim: int, other_dim: int):
      """
      Determine which type of matmul operation should be executed for the given tensor dimensions.
  
      Args:
          input_dim (int): the number of dimensions for the input tensor
          other_dim (int): the number of dimensions for the other tensor
      """
      if input_dim == 1 and other_dim == 1:
          matmul_type = MatMulType.DOT
      elif input_dim in [1, 2] and other_dim == 2:
          matmul_type = MatMulType.MM
      elif input_dim == 2 and other_dim == 1:
          matmul_type = MatMulType.MV
      elif (
          input_dim >= 1
          and other_dim >= 1
          and (input_dim > 2 or other_dim > 2)
      ):
          matmul_type = MatMulType.BMM
      else:
          raise ValueError(
              f"The input and other tensors are of {input_dim} and {other_dim} which cannot used to execute matmul operation"
          )
      return matmul_type
  
imports: import operator
  from abc import ABC, abstractmethod
  from copy import deepcopy
  from enum import Enum
  from functools import reduce
  from typing import Dict, List, Union
  import torch
  from colossalai.auto_parallel.tensor_shard.utils.broadcast import BroadcastType, get_broadcast_dim_info, get_broadcast_shape
  from colossalai.tensor.sharding_spec import ShardingSpecException
  from ..sharding_strategy import OperationData, OperationDataType, ShardingStrategy
  from ..utils import recover_sharding_spec_for_broadcast_shape
  from .node_handler import MetaInfoNodeHandler
  from .registry import operator_registry
  from .strategy import BatchedMatMulStrategyGenerator, DotProductStrategyGenerator, LinearProjectionStrategyGenerator, MatVecStrategyGenerator, StrategyGenerator
label_desc: Use image node_id 1 for calling a global function with example usage: get_matmul_type(input_dim, other_dim) and returns: matmul_type