class_docstr: A Pipeline object represents a collection of named expressions to be
  compiled and executed by a PipelineEngine.
  
  A Pipeline has two important attributes: 'columns', a dictionary of named
  `Term` instances, and 'screen', a Filter representing criteria for
  including an asset in the results of a Pipeline.
  
  To compute a pipeline in the context of a TradingAlgorithm, users must call
  ``attach_pipeline`` in their ``initialize`` function to register that the
  pipeline should be computed each trading day.  The outputs of a pipeline on
  a given day can be accessed by calling ``pipeline_output`` in
  ``handle_data`` or ``before_trading_start``.
  
  Parameters
  ----------
  columns : dict, optional
      Initial columns.
  screen : catalyst.pipeline.term.Filter, optional
      Initial screen.
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docstr: The columns registered with this pipeline.
docstr_tok: ["The","columns","registered","with","this","pipeline","."]
code: def columns(self):
      """
      The columns registered with this pipeline.
      """
      return self._columns
  
file_path: catalyst/catalyst/pipeline/pipeline.py
imports: from catalyst.errors import UnsupportedPipelineOutput
  from catalyst.utils.input_validation import expect_element, expect_types, optional
  from .graph import ExecutionPlan, TermGraph
  from .filters import Filter
  from .term import AssetExists, ComputableTerm, Term
label_desc: Use image node_id 2 for calling the Pipeline obj's underlying member method code with example usage: obj.columns() and returns: self