class_docstr: Model object for tracking progress of a Pipeline execution.
  
  Parameters
  ----------
  nterms : int
      Number of terms in the execution plan of the Pipeline being run.
  start_date : pd.Timestamp
      Start date of the range over which ``plan`` will be computed.
  end_date : pd.Timestamp
      End date of the range over which ``plan`` will be computed.
  
  Methods
  -------
  start_chunk(start_date, end_date)
  finish_chunk(start_date, end_date)
  load_precomputed_terms(terms)
  start_load_terms(terms)
  finish_load_terms(terms)
  start_compute_term(term)
  finish_compute_term(term)
  finish(success)
  
  Attributes
  ----------
  state : {'init', 'loading', 'computing', 'error', 'success'}
      Current state of the execution.
  percent_complete : float
      Percent of execution that has been completed, on a scale from 0 to 100.
  execution_time : float
      Number of seconds that the execution required. Only available if state
      is 'error' or 'success'.
  execution_bounds : (pd.Timestamp, pd.Timestamp)
      Pair of (start_date, end_date) for the entire execution.
  current_chunk_bounds : (pd.Timestamp, pd.Timestamp)
      Pair of (start_date, end_date) for the currently executing chunk.
  current_work : [zipline.pipeline.Term]
      List of terms currently being loaded or computed.
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code: def start_compute_term(self, term):
      self._state = "computing"
      self._current_work = [term]
  
imports: from collections import namedtuple
  import time
  from interface import implements
  from zipline.utils.compat import contextmanager, escape_html
  from zipline.utils.string_formatting import bulleted_list
  from .iface import PipelineHooks
label_desc: Use image node_id 12 for calling the ProgressModel obj's underlying member method code with example usage: obj.start_compute_term(term) without return types