message: Line too long (line-too-long) fix
diff: @@ -2391,7 +2391,8 @@ class _BucketizedColumn(_DenseColumn, _CategoricalColumn,
  array_ops.reshape(input_tensor, (-1,)) +
  (len(self.boundaries) + 1) * i2)
  - indices = math_ops.cast(array_ops.transpose(array_ops.stack((i1, i2))), dtypes.int64)
  + indices = math_ops.cast(
  + array_ops.transpose(array_ops.stack((i1, i2))), dtypes.int64)
  dense_shape = math_ops.cast(array_ops.stack(
  [batch_size, source_dimension]), dtypes.int64)
  sparse_tensor = sparse_tensor_lib.SparseTensor(
  @@ -2925,7 +2926,8 @@ class _IdentityCategoricalColumn(
  self.key, input_tensor.dtype))
  values = math_ops.cast(input_tensor.values, dtypes.int64, name='values')
  - num_buckets = math_ops.cast(self.num_buckets, dtypes.int64, name='num_buckets')
  + num_buckets = math_ops.cast(
  + self.num_buckets, dtypes.int64, name='num_buckets')
  zero = math_ops.cast(0, dtypes.int64, name='zero')
  if self.default_value is None:
  # Fail if values are out-of-range.
  @@ -3256,7 +3258,8 @@ def _sequence_length_from_sparse_tensor(sp_tensor, num_elements=1):
  # Example: orig tensor [[1, 2], [3]], col_ids = (0, 1, 1),
  # row_ids = (0, 0, 1), seq_length = [2, 1]. If num_elements = 2,
  # these will get grouped, and the final seq_length is [1, 1]
  - seq_length = math_ops.cast(math_ops.ceil(seq_length / num_elements), dtypes.int64)
  + seq_length = math_ops.cast(
  + math_ops.ceil(seq_length / num_elements), dtypes.int64)
  # If the last n rows do not have ids, seq_length will have shape
  # [batch_size - n]. Pad the remaining values with zeros.
  