class_docstr: Provides request headers indicating the type of Databricks environment from which a request
  was made.
class_docstr_tok: ["Provides","request","headers","indicating","the","type","of","Databricks","environment","from","which","a","request","was","made","."]
code: def request_headers(self):
      request_headers = {}
      if databricks_utils.is_in_databricks_notebook():
          request_headers[
              "notebook_id"
          ] = databricks_utils.get_notebook_id()
      if databricks_utils.is_in_databricks_job():
          request_headers["job_id"] = databricks_utils.get_job_id()
          request_headers[
              "job_run_id"
          ] = databricks_utils.get_job_run_id()
          request_headers["job_type"] = databricks_utils.get_job_type()
      if databricks_utils.is_in_cluster():
          request_headers[
              "cluster_id"
          ] = databricks_utils.get_cluster_id()
      command_run_id = databricks_utils.get_command_run_id()
      if command_run_id is not None:
          request_headers["command_run_id"] = command_run_id
  
      return request_headers
  
imports: from mlflow.tracking.request_header.abstract_request_header_provider import RequestHeaderProvider
  from mlflow.utils import databricks_utils
label_desc: Use image node_id 2 for calling the DatabricksRequestHeaderProvider obj's underlying member method code with example usage: obj.request_headers() and returns: request_headers