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Class wrapping dynamic-sized, per-time-step, write-once Tensor arrays.

Used in the notebooks

Used in the guide Used in the tutorials

This class is meant to be used with dynamic iteration primitives such as while_loop and map_fn. It supports gradient back-propagation via special "flow" control flow dependencies.

Example 1: Plain reading and writing.

ta = tf.TensorArray(tf.float32, size=0, dynamic_size=True, clear_after_read=False)
ta = ta.write(0, 10)
ta = ta.write(1, 20)
ta = ta.write(2, 30)
<tf.Tensor: shape=(), dtype=float32, numpy=10.0>
<tf.Tensor: shape=(), dtype=float32, numpy=20.0>
<tf.Tensor: shape=(), dtype=float32, numpy=30.0>