def from_sparse(st_input, name=None, row_splits_dtype=tf.int64):
return wrap(
tf.RaggedTensor.from_sparse,
st_input,
name=name,
row_splits_dtype=row_splits_dtype,
)
def from_row_lengths(values, row_lengths, name=None, validate=True):
return wrap(
tf.RaggedTensor.from_row_lengths,
values,
row_lengths,
name=name,
validate=validate,
)
def from_nested_row_lengths(flat_values,
nested_row_lengths,
name=None,
validate=True):
return wrap(
tf.RaggedTensor.from_nested_row_lengths,
flat_values,
nested_row_lengths,
name=name,
validate=validate,
)
def from_row_splits(values,
row_splits,
name: Optional[str] = None,
validate: bool = True):
return wrap(
tf.RaggedTensor.from_row_splits,
values,
row_splits,
name=name,
validate=validate,
)
def from_uniform_row_length(values,
uniform_row_length,
nrows=None,
validate=True,
name=None):
return wrap(
tf.RaggedTensor.from_uniform_row_length,
values,
uniform_row_length,
nrows,
name=name,
validate=validate,
)
def from_nested_value_rowids(flat_values,
nested_value_rowids,
nested_nrows=None,
name=None,
validate=True):
return wrap(
tf.RaggedTensor.from_nested_value_rowids,
flat_values,
nested_value_rowids,
nested_nrows,
name=name,
validate=validate,
)
def from_value_rowids(values,
value_rowids,
nrows=None,
name=None,
validate=True):
return wrap(
tf.RaggedTensor.from_value_rowids,
values,
value_rowids,
nrows,
name=name,
validate=validate,
)
def from_tensor(
tensor,
lengths=None,
padding=None,
ragged_rank=1,
name=None,
row_splits_dtype=tf.int64,
):
return wrap(
tf.RaggedTensor.from_tensor,
tensor,
lengths,
padding,
ragged_rank=1,
name=None,
row_splits_dtype=row_splits_dtype,
)
def SparseTensor(indices, values, dense_shape):
return wrap(tf.SparseTensor, indices, values, dense_shape)
