class_docstr: A Filter that computes to True only for the given asset.
class_docstr_tok: ["A","Filter","that","computes","to","True","only","for","the","given","asset","."]
code: def _compute(self, arrays, dates, assets, mask):
      is_my_asset = assets == self._asset.sid
      out = repeat_first_axis(is_my_asset, len(mask))
      # Raise an exception if `self._asset` does not exist for the entirety
      # of the timeframe over which we are computing.
      if (is_my_asset.sum() != 1) or ((out & mask).sum() != len(mask)):
          raise NonExistentAssetInTimeFrame(
              asset=self._asset,
              start_date=dates[0],
              end_date=dates[-1],
          )
      return out
  
imports: from itertools import chain
  from operator import attrgetter
  from numpy import float64, nan, nanpercentile
  from catalyst.errors import BadPercentileBounds, NonExistentAssetInTimeFrame, UnsupportedDataType
  from catalyst.lib.labelarray import LabelArray
  from catalyst.lib.rank import is_missing
  from catalyst.pipeline.expression import BadBinaryOperator, FILTER_BINOPS, method_name_for_op, NumericalExpression
  from catalyst.pipeline.mixins import AliasedMixin, CustomTermMixin, DownsampledMixin, LatestMixin, PositiveWindowLengthMixin, RestrictedDTypeMixin, SingleInputMixin
  from catalyst.pipeline.term import ComputableTerm, Term
  from catalyst.utils.input_validation import expect_types
  from catalyst.utils.memoize import classlazyval
  from catalyst.utils.numpy_utils import bool_dtype, repeat_first_axis
label_desc: Use image node_id 4 for calling the SingleAsset obj's underlying member method code with example usage: obj._compute(arrays, dates, assets, mask) and returns: out