| __init__(self, cell, T) (defined in caffe2.python.rnn_cell.UnrolledCell) | caffe2.python.rnn_cell.UnrolledCell | |
| __init__(self, name=None, forward_only=False, initializer=None) (defined in caffe2.python.rnn_cell.RNNCell) | caffe2.python.rnn_cell.RNNCell | |
| apply(self, model, input_t, seq_lengths, states, timestep) (defined in caffe2.python.rnn_cell.RNNCell) | caffe2.python.rnn_cell.RNNCell | |
| apply_over_sequence(self, model, inputs, seq_lengths, initial_states, outputs_with_grads=None) (defined in caffe2.python.rnn_cell.UnrolledCell) | caffe2.python.rnn_cell.UnrolledCell | |
| apply_override(self, model, input_t, seq_lengths, timestep, extra_inputs=None) | caffe2.python.rnn_cell.RNNCell | |
| cell (defined in caffe2.python.rnn_cell.UnrolledCell) | caffe2.python.rnn_cell.UnrolledCell | |
| forward_only (defined in caffe2.python.rnn_cell.RNNCell) | caffe2.python.rnn_cell.RNNCell | |
| get_output_dim(self) | caffe2.python.rnn_cell.RNNCell | |
| get_output_state_index(self) | caffe2.python.rnn_cell.RNNCell | |
| get_state_names(self) | caffe2.python.rnn_cell.RNNCell | |
| get_state_names_override(self) | caffe2.python.rnn_cell.RNNCell | |
| initializer(self) (defined in caffe2.python.rnn_cell.RNNCell) | caffe2.python.rnn_cell.RNNCell | |
| initializer(self, value) (defined in caffe2.python.rnn_cell.RNNCell) | caffe2.python.rnn_cell.RNNCell | |
| name (defined in caffe2.python.rnn_cell.RNNCell) | caffe2.python.rnn_cell.RNNCell | |
| prepare_input(self, model, input_blob) | caffe2.python.rnn_cell.RNNCell | |
| recompute_blobs (defined in caffe2.python.rnn_cell.RNNCell) | caffe2.python.rnn_cell.RNNCell | |
| scope(self, name) (defined in caffe2.python.rnn_cell.RNNCell) | caffe2.python.rnn_cell.RNNCell | |
| T (defined in caffe2.python.rnn_cell.UnrolledCell) | caffe2.python.rnn_cell.UnrolledCell | |