code: def add_positional_features(
      tensor, min_timescale=1.0, max_timescale=1.0e4
  ):
      # pylint: disable=line-too-long
      """
      Implements the frequency-based positional encoding described
      in `Attention is all you Need
      <https://www.semanticscholar.org/paper/Attention-Is-All-You-Need-Vaswani-Shazeer/0737da0767d77606169cbf4187b83e1ab62f6077>`_ .
  
      Adds sinusoids of different frequencies to a ``Tensor``. A sinusoid of a
      different frequency and phase is added to each dimension of the input ``Tensor``.
      This allows the attention heads to use absolute and relative positions.
  
      The number of timescales is equal to hidden_dim / 2 within the range
      (min_timescale, max_timescale). For each timescale, the two sinusoidal
      signals sin(timestep / timescale) and cos(timestep / timescale) are
      generated and concatenated along the hidden_dim dimension.
  
      Parameters
      ----------
      tensor : ``torch.Tensor``
          a Tensor with shape (batch_size, timesteps, hidden_dim).
      min_timescale : ``float``, optional (default = 1.0)
          The smallest timescale to use.
      max_timescale : ``float``, optional (default = 1.0e4)
          The largest timescale to use.
  
      Returns
      -------
      The input tensor augmented with the sinusoidal frequencies.
      """
      _, timesteps, hidden_dim = tensor.size()
  
      timestep_range = get_range_vector(
          timesteps, get_device_of(tensor)
      ).data.float()
      # We're generating both cos and sin frequencies,
      # so half for each.
      num_timescales = hidden_dim // 2
      timescale_range = get_range_vector(
          num_timescales, get_device_of(tensor)
      ).data.float()
  
      log_timescale_increments = math.log(
          float(max_timescale) / float(min_timescale)
      ) / float(num_timescales - 1)
      inverse_timescales = min_timescale * torch.exp(
          timescale_range * -log_timescale_increments
      )
  
      # Broadcasted multiplication - shape (timesteps, num_timescales)
      scaled_time = timestep_range.unsqueeze(
          1
      ) * inverse_timescales.unsqueeze(0)
      # shape (timesteps, 2 * num_timescales)
      sinusoids = torch.cat(
          [torch.sin(scaled_time), torch.cos(scaled_time)], 1
      )
      if hidden_dim % 2 != 0:
          # if the number of dimensions is odd, the cos and sin
          # timescales had size (hidden_dim - 1) / 2, so we need
          # to add a row of zeros to make up the difference.
          sinusoids = torch.cat(
              [sinusoids, sinusoids.new_zeros(timesteps, 1)], 1
          )
      return tensor + sinusoids.unsqueeze(0)
  
imports: from __future__ import division
  from __future__ import absolute_import
  from collections import defaultdict
  import logging
  import math
  import torch
  from allennlp.common.checks import ConfigurationError
label_desc: Use image node_id 33 for calling a global function with example usage: add_positional_features(tensor, min_timescale, max_timescale) and returns: unknown