Creates a recurrent neural network specified by RNNCell `cell`. Performs fully dynamic unrolling of `inputs`. Example: ```python # create a

parametric ReLU activation

Infer the dtype of an RNN state. Args: explicit_dtype: explicitly declared dtype or None. state: RNN's hidden state. Must be a Tensor or a

Creates a recurrent neural network specified by RNNCell `cell`. The simplest form of RNN network generated is: ```python state = cell.zero_s

Get static input batch size if available, with fallback to the dynamic one. Args: flat_input: An iterable of time major input Tensors of shape

Reverse a list of Tensors up to specified lengths. Args: input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features)

Calculate one step of a dynamic RNN minibatch. Returns an (output, state) pair conditioned on the sequence_lengths. When skip_conditionals=False,

Summary Args: raw_arr (TYPE): Description Returns: TYPE: Description

Summary

Internal implementation of Dynamic RNN. Args: cell: An instance of RNNCell. inputs: A `Tensor` of shape [time, batch_size, input_size], or

Transpose the batch and time dimensions of a Tensor. Retains as much of the static shape information as possible. Args: x: A tensor of rank

Gated recurrent unit (GRU) with nunits cells.

Summary Args: *args (TYPE): Description **kwargs (TYPE): Description Returns:

Summary Args: instance (TYPE): Description cls (TYPE): Description Returns:

Summary Args: func (TYPE): Description

Run RNN step. Pass through either no or some past state.

Take a time step of the dynamic RNN. Args: time: int32 scalar Tensor. output_ta_t: List of `TensorArray`s that represent the output.

Creates a dynamic version of bidirectional recurrent neural network. Takes input and builds independent forward and backward RNNs. The input_size

Internal while loop body for raw_rnn. Args: time: time scalar. elements_finished: batch-size vector. current_input: pos

Summary Args: raw_arr (TYPE): Description Returns: TYPE: Description

Creates an `RNN` specified by RNNCell `cell` and loop function `loop_fn`. **NOTE: This method is still in testing, and the API may change.** Thi