class_docstr: A table that can store Tensors or nested Tensors.
class_docstr_tok: ["A","table","that","can","store","Tensors","or","nested","Tensors","."]
docstr: Returns ops for writing values at the given rows.
  
  Args:
    rows: A scalar/list/tensor of location(s) to write values at.
    values: A nest of Tensors to write. If rows has more than one element,
      values can have an extra first dimension representing the batch size.
      Values must have the same structure as the tensor_spec of this class if
      `slots` is None, otherwise it must have the same structure as `slots`.
    slots: Optional list/tuple/nest of slots to write. If None, all tensors in
      the table are updated. Otherwise, only tensors with names matching the
      slots are updated.
  
  Returns:
    Ops for writing values at rows.
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code: def write(self, rows, values, slots=None):
      """Returns ops for writing values at the given rows.
  
      Args:
        rows: A scalar/list/tensor of location(s) to write values at.
        values: A nest of Tensors to write. If rows has more than one element,
          values can have an extra first dimension representing the batch size.
          Values must have the same structure as the tensor_spec of this class if
          `slots` is None, otherwise it must have the same structure as `slots`.
        slots: Optional list/tuple/nest of slots to write. If None, all tensors in
          the table are updated. Otherwise, only tensors with names matching the
          slots are updated.
  
      Returns:
        Ops for writing values at rows.
      """
      slots = slots or self._slots
      flattened_slots = tf.nest.flatten(slots)
      flattened_values = tf.nest.flatten(values)
      write_ops = [
          tf.compat.v1.scatter_update(
              self._slot2storage_map[slot], rows, value
          ).op
          for (slot, value) in zip(flattened_slots, flattened_values)
      ]
      return tf.group(*write_ops)
  
file_path: agents/tf_agents/replay_buffers/table.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.utils import common
label_desc: Use image node_id 5 for calling the Table obj's underlying member method code with example usage: obj.write(rows, values, slots) and returns: tf