code: def default_populate_initial_workspace(
      initial_workspace, root_mask_term, execution_plan, dates, assets
  ):
      """The default implementation for ``populate_initial_workspace``. This
      function returns the ``initial_workspace`` argument without making any
      modifications.
  
      Parameters
      ----------
      initial_workspace : dict[array-like]
          The initial workspace before we have populated it with any cached
          terms.
      root_mask_term : Term
          The root mask term, normally ``AssetExists()``. This is needed to
          compute the dates for individual terms.
      execution_plan : ExecutionPlan
          The execution plan for the pipeline being run.
      dates : pd.DatetimeIndex
          All of the dates being requested in this pipeline run including
          the extra dates for look back windows.
      assets : pd.Int64Index
          All of the assets that exist for the window being computed.
  
      Returns
      -------
      populated_initial_workspace : dict[term, array-like]
          The workspace to begin computations with.
      """
      return initial_workspace
  
imports: from abc import ABCMeta, abstractmethod
  from functools import partial
  from six import iteritems, with_metaclass, viewkeys
  from numpy import array, arange
  from pandas import DataFrame, MultiIndex
  from toolz import groupby
  from zipline.lib.adjusted_array import ensure_adjusted_array, ensure_ndarray
  from zipline.errors import NoFurtherDataError
  from zipline.utils.input_validation import expect_types
  from zipline.utils.numpy_utils import as_column, repeat_first_axis, repeat_last_axis
  from zipline.utils.pandas_utils import explode
  from zipline.utils.string_formatting import bulleted_list
  from .domain import Domain, GENERIC
  from .graph import maybe_specialize
  from .hooks import DelegatingHooks
  from .term import AssetExists, InputDates, LoadableTerm
  from zipline.utils.date_utils import compute_date_range_chunks
  from zipline.utils.pandas_utils import categorical_df_concat
label_desc: Use image node_id 1 for calling a global function with example usage: default_populate_initial_workspace(initial_workspace, root_mask_term, execution_plan, dates, assets) and returns: initial_workspace