code: def apply_adjustments(
      self, dates, assets, baseline_values, adjustments
  ):
      min_date, max_date = dates[[0, -1]]
      # HACK: Simulate the coercion to float64 we do in adjusted_array.  This
      # should be removed when AdjustedArray properly supports
      # non-floating-point types.
      orig_dtype = baseline_values.dtype
      values = baseline_values.astype(float64).copy()
      for eff_date_secs, ratio, sid in adjustments.itertuples(
          index=False
      ):
          eff_date = seconds_to_timestamp(eff_date_secs)
          # Don't apply adjustments that aren't in the current date range.
          if eff_date not in dates:
              continue
          eff_date_loc = dates.get_loc(eff_date)
          asset_col = assets.get_loc(sid)
          # Apply ratio multiplicatively to the asset column on all rows less
          # than or equal adjustment effective date.
          values[: eff_date_loc + 1, asset_col] *= ratio
      return values.astype(orig_dtype)
  
imports: from nose_parameterized import parameterized
  from numpy import arange, datetime64, float64, ones, uint32
  from numpy.testing import assert_allclose, assert_array_equal
  from pandas import concat, DataFrame, Int64Index, Timestamp
  from pandas.util.testing import assert_frame_equal
  from toolz.curried.operator import getitem
  from zipline.lib.adjustment import Float64Multiply
  from zipline.pipeline.domain import US_EQUITIES
  from zipline.pipeline.loaders.synthetic import NullAdjustmentReader, make_bar_data, expected_bar_values_2d
  from zipline.pipeline.loaders.equity_pricing_loader import USEquityPricingLoader
  from zipline.errors import WindowLengthTooLong
  from zipline.pipeline.data import USEquityPricing
  from zipline.testing import seconds_to_timestamp, str_to_seconds, MockDailyBarReader
  from zipline.testing.fixtures import WithAdjustmentReader, ZiplineTestCase
label_desc: Use image node_id 14 for calling the USEquityPricingLoaderTestCase obj's underlying member method code with example usage: obj.apply_adjustments(dates, assets, baseline_values, adjustments) and returns: values