class_docstr: A TensorConstructorHandler which deals with the sharding strategies for tensor constructor operations, such as torch.arange.
class_docstr_tok: ["A","TensorConstructorHandler","which","deals","with","the","sharding","strategies","for","tensor","constructor","operations",",","such","as","torch.arange","."]
code: def get_operation_data_mapping(self) -> Dict[str, OperationData]:
      output_data = self.node._meta_data
      physical_output_operand = OperationData(
          name=str(self.node),
          type=OperationDataType.OUTPUT,
          data=output_data,
      )
  
      mapping = {"output": physical_output_operand}
  
      return mapping
  
file_path: colossalai/colossalai/auto_parallel/tensor_shard/node_handler/tensor_constructor_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
  from .strategy.tensor_constructor_generator import TensorConstructorGenerator
label_desc: Use image node_id 2 for calling the TensorConstructorHandler obj's underlying member method code with example usage: obj.get_operation_data_mapping() and returns: mapping