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: Update state with predictions and targets.
  
  Raises:
      ValueError:
          If ``preds`` or ``target`` has multiple outputs when ``num_outputs=1``
docstr_tok: ["Update","state","with","predictions","and","targets",".","Raises",":","ValueError",":","If","``","preds","``","or","``","target","``","has","multiple","outputs","when","``","num_outputs=1","``"]
code: def update(self, preds: Tensor, target: Tensor) -> None:
      """Update state with predictions and targets.
  
      Raises:
          ValueError:
              If ``preds`` or ``target`` has multiple outputs when ``num_outputs=1``
  
      """
      sum_log_cosh_error, num_obs = _log_cosh_error_update(
          preds, target, self.num_outputs
      )
      self.sum_log_cosh_error += sum_log_cosh_error
      self.total += num_obs
  
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 2 for calling the LogCoshError obj's underlying member method code with example usage: obj.update(preds, target) without return types