class_docstr: Compute the `LogCosh Error`_.
  
  .. math:: \text{LogCoshError} = \log\left(\frac{\exp(\hat{y} - y) + \exp(\hat{y - y})}{2}\right)
  
  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`): Estimated labels with shape ``(batch_size,)``
    or ``(batch_size, num_outputs)``
  - ``target`` (:class:`~torch.Tensor`): Ground truth labels with shape ``(batch_size,)``
    or ``(batch_size, num_outputs)``
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``log_cosh_error`` (:class:`~torch.Tensor`): A tensor with the log cosh error
  
  Args:
      num_outputs: Number of outputs in multioutput setting
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example (single output regression)::
      >>> from torchmetrics.regression import LogCoshError
      >>> preds = torch.tensor([3.0, 5.0, 2.5, 7.0])
      >>> target = torch.tensor([2.5, 5.0, 4.0, 8.0])
      >>> log_cosh_error = LogCoshError()
      >>> log_cosh_error(preds, target)
      tensor(0.3523)
  
  Example (multi output regression)::
      >>> from torchmetrics.regression import LogCoshError
      >>> preds = torch.tensor([[3.0, 5.0, 1.2], [-2.1, 2.5, 7.0]])
      >>> target = torch.tensor([[2.5, 5.0, 1.3], [0.3, 4.0, 8.0]])
      >>> log_cosh_error = LogCoshError(num_outputs=3)
      >>> log_cosh_error(preds, target)
      tensor([0.9176, 0.4277, 0.2194])
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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 LogCoshError
      >>> metric = LogCoshError()
      >>> 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 LogCoshError
      >>> metric = LogCoshError()
      >>> values = []
      >>> for _ in range(10):
      ...     values.append(metric(randn(10,), randn(10,)))
      >>> fig, ax = metric.plot(values)
docstr_tok: ["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","LogCoshError",">",">",">","metric","=","LogCoshError","(",")",">",">",">","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","LogCoshError",">",">",">","metric","=","LogCoshError","(",")",">",">",">","values","=","[","]",">",">",">","for","_","in","range","(","10",")",":","...","values.append","(","metric","(","randn","(","10",",",")",",","randn","(","10",",",")",")",")",">",">",">","fig",",","ax","=","metric.plot","(","values",")"]
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 LogCoshError
          >>> metric = LogCoshError()
          >>> 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 LogCoshError
          >>> metric = LogCoshError()
          >>> 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/log_cosh.py
imports: from typing import Any, Optional, Sequence, Union
  import torch
  from torch import Tensor
  from torchmetrics.functional.regression.log_cosh import _log_cosh_error_compute, _log_cosh_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 LogCoshError obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self