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: Compute weighted mean absolute percentage error over state.
docstr_tok: ["Compute","weighted","mean","absolute","percentage","error","over","state","."]
code: def compute(self) -> Tensor:
      """Compute weighted mean absolute percentage error over state."""
      return _weighted_mean_absolute_percentage_error_compute(
          self.sum_abs_error, self.sum_scale
      )
  
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 3 for calling the WeightedMeanAbsolutePercentageError obj's underlying member method code with example usage: obj.compute() and returns: _weighted_mean_absolute_percentage_error_compute