class_docstr: A class to store nested objects in a collection of numpy arrays.
  
  If a data_spec of `{'foo': ArraySpec(shape=(4,), dtype=np.uint8), 'bar':
  ArraySpec(shape=(3, 7), dtype=np.float32)}` were used, then this would create
  two arrays, one for the 'foo' key and one for the 'bar' key. The .get and
  .set methods would return/take Python dictionaries, but break down the
  component arrays before storing them.
docstr: Set table_idx to value.
docstr_tok: ["Set","table_idx","to","value","."]
code: def set(self, table_idx, value):
      """Set table_idx to value."""
      for nest_idx, element in enumerate(tf.nest.flatten(value)):
          self._array(nest_idx)[table_idx] = element
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import io
  import numpy
  import tensorflow
  from tf_agents.specs import array_spec
  from tensorflow.python.trackable import base
  from tensorflow.python.trackable import data_structures
label_desc: Use image node_id 4 for calling the NumpyStorage obj's underlying member method code with example usage: obj.set(table_idx, value) without return types