class_docstr: A ViewHandler which deals with the sharding strategies for Reshape Op, such as torch.reshape.
code: def get_operation_data_mapping(self) -> Dict[str, OperationData]:
      # use transposed shape for strategies
      # the strategies will be transformed back to its original shape in self.post_process
  
      # check if the input operand is a parameter
      if isinstance(
          self.node.args[0]._meta_data, torch.nn.parameter.Parameter
      ):
          data_type = OperationDataType.PARAM
      else:
          data_type = OperationDataType.ARG
  
      input_data = self.node.args[0]._meta_data
      physical_input_operand = OperationData(
          name=str(self.node.args[0]), type=data_type, data=input_data
      )
  
      target_shape = self.node._meta_data.shape
      physical_shape_operand = OperationData(
          name="tgt_shape",
          type=OperationDataType.ARG,
          data=target_shape,
      )
  
      output_data = self.node._meta_data
      physical_output_operand = OperationData(
          name=str(self.node),
          type=OperationDataType.OUTPUT,
          data=output_data,
      )
  
      mapping = {
          "input": physical_input_operand,
          "tgt_shape": physical_shape_operand,
          "output": physical_output_operand,
      }
  
      return mapping
  
file_path: colossalai/colossalai/auto_parallel/tensor_shard/node_handler/view_handler.py
imports: from typing import Dict, List
  import torch
  from ..sharding_strategy import OperationData, OperationDataType
  from .node_handler import NodeHandler
  from .registry import operator_registry
  from .strategy import StrategyGenerator, ViewGenerator
label_desc: Use image node_id 2 for calling the ViewHandler obj's underlying member method code with example usage: obj.get_operation_data_mapping() and returns: mapping