class_docstr: Lambda step for workflow.
docstr: Constructs a LambdaStep.
  
  Args:
      name (str): The name of the lambda step.
      display_name (str): The display name of the Lambda step.
      description (str): The description of the Lambda step.
      lambda_func (str): An instance of sagemaker.lambda_helper.Lambda.
          If lambda arn is specified in the instance, LambdaStep just invokes the function,
          else lambda function will be created while creating the pipeline.
      inputs (dict): Input arguments that will be provided
          to the lambda function.
      outputs (List[LambdaOutput]): List of outputs from the lambda function.
      cache_config (CacheConfig):  A `sagemaker.workflow.steps.CacheConfig` instance.
      depends_on (List[Union[str, Step, StepCollection]]): A list of `Step`/`StepCollection`
          names or `Step` instances or `StepCollection` instances that this `LambdaStep`
          depends on.
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code: def __init__(
      self,
      name: str,
      lambda_func: Lambda,
      display_name: str = None,
      description: str = None,
      inputs: dict = None,
      outputs: List[LambdaOutput] = None,
      cache_config: CacheConfig = None,
      depends_on: Optional[
          List[Union[str, Step, StepCollection]]
      ] = None,
  ):
      """Constructs a LambdaStep.
  
      Args:
          name (str): The name of the lambda step.
          display_name (str): The display name of the Lambda step.
          description (str): The description of the Lambda step.
          lambda_func (str): An instance of sagemaker.lambda_helper.Lambda.
              If lambda arn is specified in the instance, LambdaStep just invokes the function,
              else lambda function will be created while creating the pipeline.
          inputs (dict): Input arguments that will be provided
              to the lambda function.
          outputs (List[LambdaOutput]): List of outputs from the lambda function.
          cache_config (CacheConfig):  A `sagemaker.workflow.steps.CacheConfig` instance.
          depends_on (List[Union[str, Step, StepCollection]]): A list of `Step`/`StepCollection`
              names or `Step` instances or `StepCollection` instances that this `LambdaStep`
              depends on.
      """
      super(LambdaStep, self).__init__(
          name,
          display_name,
          description,
          StepTypeEnum.LAMBDA,
          depends_on,
      )
      self.lambda_func = lambda_func
      self.outputs = outputs if outputs is not None else []
      self.cache_config = cache_config
      self.inputs = inputs if inputs is not None else {}
  
      root_prop = Properties(step_name=name)
  
      property_dict = {}
      for output in self.outputs:
          property_dict[output.output_name] = Properties(
              step_name=name,
              path=f"OutputParameters['{output.output_name}']",
          )
  
      root_prop.__dict__["Outputs"] = property_dict
      self._properties = root_prop
  
imports: from __future__ import absolute_import
  from typing import List, Dict, Optional, Union
  from enum import Enum
  import warnings
  import attr
  from sagemaker.workflow.entities import RequestType
  from sagemaker.workflow.properties import Properties
  from sagemaker.workflow.entities import DefaultEnumMeta
  from sagemaker.workflow.step_collections import StepCollection
  from sagemaker.workflow.steps import Step, StepTypeEnum, CacheConfig
  from sagemaker.lambda_helper import Lambda
label_desc: Use image node_id 1 to create a new LambdaStep object from inherited base classes: Step with example: obj = LambdaStep(name, lambda_func, display_name, description, inputs, outputs, cache_config, depends_on)