def _as_variant_tensor(self):
return gen_bigtable_ops.bigtable_scan_dataset(
table=self._table._resource, # pylint: disable=protected-access
prefix=self._prefix,
start_key=self._start,
end_key=self._end,
column_families=self._column_families,
columns=self._columns,
probability=self._probability)
def _as_variant_tensor(self):
return gen_bigtable_ops.bigtable_scan_dataset(
table=self._table._resource, # pylint: disable=protected-access
prefix=self._prefix,
start_key=self._start,
end_key=self._end,
column_families=self._column_families,
columns=self._columns,
probability=self._probability)
def __init__(self, table, prefix, start, end, normalized, probability):
self._table = table
self._prefix = prefix
self._start = start
self._end = end
self._column_families = [i[0] for i in normalized]
self._columns = [i[1] for i in normalized]
self._probability = probability
self._num_outputs = len(normalized) + 1 # 1 for row key
variant_tensor = gen_bigtable_ops.bigtable_scan_dataset(
table=self._table._resource, # pylint: disable=protected-access
prefix=self._prefix,
start_key=self._start,
end_key=self._end,
column_families=self._column_families,
columns=self._columns,
probability=self._probability)
super(_BigtableScanDataset, self).__init__(variant_tensor)
def __init__(self, table, prefix, start, end, normalized, probability):
self._table = table
self._prefix = prefix
self._start = start
self._end = end
self._column_families = [i[0] for i in normalized]
self._columns = [i[1] for i in normalized]
self._probability = probability
self._num_outputs = len(normalized) + 1 # 1 for row key
variant_tensor = gen_bigtable_ops.bigtable_scan_dataset(
table=self._table._resource, # pylint: disable=protected-access
prefix=self._prefix,
start_key=self._start,
end_key=self._end,
column_families=self._column_families,
columns=self._columns,
probability=self._probability)
super(_BigtableScanDataset, self).__init__(variant_tensor)