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 values for the given rows.
  
  Args:
    rows: A scalar/list/tensor of location(s) to read values from. If rows is
      a scalar, a single value is returned without a batch dimension. If rows
      is a list of integers or a rank-1 int Tensor a batch of values will be
      returned with each Tensor having an extra first dimension equal to the
      length of rows.
    slots: Optional list/tuple/nest of slots to read from. If None, all
      tensors at the given rows are retrieved and the return value has the
      same structure as the tensor_spec. Otherwise, only tensors with names
      matching the slots are retrieved, and the return value has the same
      structure as slots.
  
  Returns:
    Values at given rows.
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code: def read(self, rows, slots=None):
      """Returns values for the given rows.
  
      Args:
        rows: A scalar/list/tensor of location(s) to read values from. If rows is
          a scalar, a single value is returned without a batch dimension. If rows
          is a list of integers or a rank-1 int Tensor a batch of values will be
          returned with each Tensor having an extra first dimension equal to the
          length of rows.
        slots: Optional list/tuple/nest of slots to read from. If None, all
          tensors at the given rows are retrieved and the return value has the
          same structure as the tensor_spec. Otherwise, only tensors with names
          matching the slots are retrieved, and the return value has the same
          structure as slots.
  
      Returns:
        Values at given rows.
      """
      slots = slots or self._slots
      flattened_slots = tf.nest.flatten(slots)
      values = [
          self._slot2storage_map[slot].sparse_read(rows)
          for slot in flattened_slots
      ]
      return tf.nest.pack_sequence_as(slots, values)
  
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 4 for calling the Table obj's underlying member method code with example usage: obj.read(rows, slots) and returns: tf