class_docstr: Perplexity measures how well a language model predicts a text sample.
  
  It's calculated as the average number of bits per word a model needs to represent the sample.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): Logits or a unnormalized score assigned to each token in a sequence with shape
      [batch_size, seq_len, vocab_size], which is the output of a language model. Scores will be normalized internally
      using softmax.
  - ``target`` (:class:`~torch.Tensor`): Ground truth values with a shape [batch_size, seq_len]
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``perp`` (:class:`~torch.Tensor`): A tensor with the perplexity score
  
  Args:
      ignore_index: Integer specifying a target class to ignore.
          If given, this class index does not contribute to the returned score.
      kwargs:
          Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Examples:
      >>> from torchmetrics.text import Perplexity
      >>> import torch
      >>> gen = torch.manual_seed(42)
      >>> preds = torch.rand(2, 8, 5, generator=gen)
      >>> target = torch.randint(5, (2, 8), generator=gen)
      >>> target[0, 6:] = -100
      >>> perp = Perplexity(ignore_index=-100)
      >>> perp(preds, target)
      tensor(5.8540)
docstr: Compute the Perplexity.
code: def compute(self) -> Tensor:
      """Compute the Perplexity."""
      return _perplexity_compute(self.total_log_probs, self.count)
  
file_path: torchmetrics/src/torchmetrics/text/perplexity.py
imports: from typing import Any, Dict, Optional, Sequence, Union
  from torch import Tensor, tensor
  from torchmetrics.functional.text.perplexity import _perplexity_compute, _perplexity_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 3 for calling the Perplexity obj's underlying member method code with example usage: obj.compute() and returns: _perplexity_compute