How do I write a dagster op that accepts a parameter either as a config or as a dependency from an upstream op

To write a Dagster op that can accept a parameter either as a config or as a dependency from an upstream op, you can define an op with an input that can be populated by the output of an upstream op, or by a config value if there is no upstream op providing that input. Here's a step-by-step guide based on the documentation: 1. Define an op with an input that has a default value. This allows the op to run without an upstream dependency if a config value is not provided. 2. Use the

@op

decorator to define your op function. Inside the function, you can access the input value which could be provided by an upstream op or by the config. 3. When defining the job that includes this op, you can either connect it to an upstream op that provides the necessary input or provide a config value for the input. Here's an example based on the documentation:

python from dagster import op, In, job  # Define an op with an input that has a default value @op(ins={"input_param": In(default_value="default")}) def my_op(input_param):     # The op can use the input_param which could be from an upstream op or config     pass  # Define a job that includes the op @job def my_job():     # You can connect my_op to an upstream op that provides input_param     # or provide a config value for input_param when executing the job     my_op()

When executing the job, you can provide a config for

input_param

if there is no upstream op connected to `my_op`:

python my_job.execute_in_process(     run_config={         "ops": {             "my_op": {                 "inputs": {                     "input_param": "configured value"                 }             }         }     } )

If there is an upstream op that provides the

input_param

, you would connect it in the job definition, and the value from the upstream op would be used instead of the config. This approach allows you to design flexible ops that can receive parameters from different sources, making your data pipelines more adaptable to various scenarios.