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 recover(
      self,
      op_data_mapping: Dict[str, OperationData],
      strategy: ShardingStrategy,
  ):
      op_data_mapping["input"]
      op_data_mapping["other"]
  
      def _remove_padded_dim(key, strategy):
          op_data = op_data_mapping[key]
          sharding_spec = strategy.get_sharding_spec_by_name(
              op_data.name
          )
          tensor_shape = list(sharding_spec.entire_shape)
          dim_partition_list = [None] * len(tensor_shape)
  
          # padded dim is a negative number as the padded dim must be a matrix dim
          padded_dim = self.padded_dim_mapping[key]
  
          # compute the new dim partition
          for (
              tensor_dim,
              mesh_dims,
          ) in sharding_spec.dim_partition_dict.items():
              dim_partition_list[tensor_dim] = mesh_dims
          dim_partition_list.pop(padded_dim)
          unpadded_dim_partition_list = {
              k: v
              for k, v in enumerate(dim_partition_list)
              if v is not None
          }
  
          # compute unpadded tensor shape
          tensor_shape.pop(padded_dim)
  
          assert tensor_shape == list(
              op_data.data.shape
          ), f"{tensor_shape} vs {list(op_data.data.shape)}"
  
          # update sharding spec
          sharding_spec.__init__(
              sharding_spec.device_mesh,
              tensor_shape,
              unpadded_dim_partition_list,
          )
  
      # enumerate all sharding strategies
      strategies = []
      try:
          strategy_copy = strategy.clone()
  
          # only one of input and other will be padded
          if "input" in self.padded_dim_mapping:
              _remove_padded_dim("input", strategy_copy)
              _remove_padded_dim("output", strategy_copy)
          elif "other" in self.padded_dim_mapping:
              _remove_padded_dim("other", strategy_copy)
              _remove_padded_dim("output", strategy_copy)
  
          strategies.append(strategy_copy)
      except ShardingSpecException:
          pass
      return strategies
  
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 3 for calling the Padder obj's underlying member method code with example usage: obj.recover(op_data_mapping, strategy) and returns: strategies