class_docstr: Add padding to the matrix dimensions for batched matrix multiplication.
class_docstr_tok: ["Add","padding","to","the","matrix","dimensions","for","batched","matrix","multiplication","."]
code: def apply(self, shape_mapping: Dict[str, List[int]]):
      mapping_copy = deepcopy(shape_mapping)
      input_shape = mapping_copy["input"]
      other_shape = mapping_copy["other"]
  
      if len(input_shape) == 1:
          # if the input is a 1D tensor, 1 is prepended to its shape
          # and it will be removed afterwards
          input_shape.insert(0, 1)
          self.padded_dim_mapping["input"] = -2
          self.padded_dim_mapping["output"] = -2
      elif len(other_shape) == 1:
          # if the other is a 1D tensor, 1 is appended to its shape
          # and it will be removed afterwards
          other_shape = other_shape.append(1)
          self.padded_dim_mapping["other"] = -1
          self.padded_dim_mapping["output"] = -1
      return mapping_copy
  
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 2 for calling the Padder obj's underlying member method code with example usage: obj.apply(shape_mapping) and returns: mapping_copy