class_docstr: An RNN core with a custom `unroll` function.
class_docstr_tok: ["An","RNN","core","with","a","custom","`","unroll","`","function","."]
docstr: A custom function for doing static unrolls over sequences.
  
  This has the same API as `snt.static_unroll`, but allows the user to specify
  their own implementation to take advantage of the structure of the network
  for better performance, e.g. by batching the feed-forward pass over the
  whole sequence.
  
  Args:
    inputs: A nest of `tf.Tensor` in time-major format.
    state: The RNN core state.
    sequence_length: How long the static_unroll should go for.
  
  Returns:
    Nested sequence output of RNN, and final state.
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code: def unroll(
      self,
      inputs: types.NestedTensor,
      state: State,
      sequence_length: int,
  ) -> Tuple[types.NestedTensor, State]:
      """A custom function for doing static unrolls over sequences.
  
      This has the same API as `snt.static_unroll`, but allows the user to specify
      their own implementation to take advantage of the structure of the network
      for better performance, e.g. by batching the feed-forward pass over the
      whole sequence.
  
      Args:
        inputs: A nest of `tf.Tensor` in time-major format.
        state: The RNN core state.
        sequence_length: How long the static_unroll should go for.
  
      Returns:
        Nested sequence output of RNN, and final state.
      """
  
file_path: acme/acme/tf/networks/base.py
imports: import abc
  from typing import Tuple, TypeVar
  from acme import types
  import sonnet
  import tensorflow_probability
label_desc: Use image node_id 1 for calling the RNNCore obj's underlying member method code with example usage: obj.unroll(inputs, state, sequence_length) without return types