class_docstr: Compute weighted mean absolute percentage error (`WMAPE`_).
  
  The output of WMAPE metric is a non-negative floating point, where the optimal value is 0. It is computes as:
  
  .. math::
      \text{WMAPE} = \frac{\sum_{t=1}^n | y_t - \hat{y}_t | }{\sum_{t=1}^n |y_t| }
  
  Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): Predictions from model
  - ``target`` (:class:`~torch.Tensor`): Ground truth float tensor with shape ``(N,d)``
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``wmape`` (:class:`~torch.Tensor`): A tensor with non-negative floating point wmape value between 0 and 1
  
  Args:
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> import torch
      >>> _ = torch.manual_seed(42)
      >>> preds = torch.randn(20,)
      >>> target = torch.randn(20,)
      >>> wmape = WeightedMeanAbsolutePercentageError()
      >>> wmape(preds, target)
      tensor(1.3967)
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docstr: Plot a single or multiple values from the metric.
  
  Args:
      val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
          If no value is provided, will automatically call `metric.compute` and plot that result.
      ax: An matplotlib axis object. If provided will add plot to that axis
  
  Returns:
      Figure and Axes object
  
  Raises:
      ModuleNotFoundError:
          If `matplotlib` is not installed
  
  .. plot::
      :scale: 75
  
      >>> from torch import randn
      >>> # Example plotting a single value
      >>> from torchmetrics.regression import WeightedMeanAbsolutePercentageError
      >>> metric = WeightedMeanAbsolutePercentageError()
      >>> metric.update(randn(10,), randn(10,))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> from torch import randn
      >>> # Example plotting multiple values
      >>> from torchmetrics.regression import WeightedMeanAbsolutePercentageError
      >>> metric = WeightedMeanAbsolutePercentageError()
      >>> values = []
      >>> for _ in range(10):
      ...     values.append(metric(randn(10,), randn(10,)))
      >>> fig, ax = metric.plot(values)
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code: def plot(
      self,
      val: Optional[Union[Tensor, Sequence[Tensor]]] = None,
      ax: Optional[_AX_TYPE] = None,
  ) -> _PLOT_OUT_TYPE:
      """Plot a single or multiple values from the metric.
  
      Args:
          val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
              If no value is provided, will automatically call `metric.compute` and plot that result.
          ax: An matplotlib axis object. If provided will add plot to that axis
  
      Returns:
          Figure and Axes object
  
      Raises:
          ModuleNotFoundError:
              If `matplotlib` is not installed
  
      .. plot::
          :scale: 75
  
          >>> from torch import randn
          >>> # Example plotting a single value
          >>> from torchmetrics.regression import WeightedMeanAbsolutePercentageError
          >>> metric = WeightedMeanAbsolutePercentageError()
          >>> metric.update(randn(10,), randn(10,))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> from torch import randn
          >>> # Example plotting multiple values
          >>> from torchmetrics.regression import WeightedMeanAbsolutePercentageError
          >>> metric = WeightedMeanAbsolutePercentageError()
          >>> values = []
          >>> for _ in range(10):
          ...     values.append(metric(randn(10,), randn(10,)))
          >>> fig, ax = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
file_path: torchmetrics/src/torchmetrics/regression/wmape.py
imports: from typing import Any, Optional, Sequence, Union
  import torch
  from torch import Tensor
  from torchmetrics.functional.regression.wmape import _weighted_mean_absolute_percentage_error_compute, _weighted_mean_absolute_percentage_error_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 4 for calling the WeightedMeanAbsolutePercentageError obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self