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)