class_docstr: Aggregate a stream of value into their maximum value.
  
  As input to ``forward`` and ``update`` the metric accepts the following input
  
  - ``value`` (:class:`~float` or :class:`~torch.Tensor`): a single float or an tensor of float values with
    arbitrary shape ``(...,)``.
  
  As output of `forward` and `compute` the metric returns the following output
  
  - ``agg`` (:class:`~torch.Tensor`): scalar float tensor with aggregated maximum value over all inputs received
  
  Args:
      nan_strategy: options:
          - ``'error'``: if any `nan` values are encountered will give a RuntimeError
          - ``'warn'``: if any `nan` values are encountered will give a warning and continue
          - ``'ignore'``: all `nan` values are silently removed
          - a float: if a float is provided will impute any `nan` values with this value
  
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Raises:
      ValueError:
          If ``nan_strategy`` is not one of ``error``, ``warn``, ``ignore`` or a float
  
  Example:
      >>> from torch import tensor
      >>> from torchmetrics.aggregation import MaxMetric
      >>> metric = MaxMetric()
      >>> metric.update(1)
      >>> metric.update(tensor([2, 3]))
      >>> metric.compute()
      tensor(3.)
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code: def __init__(
      self,
      nan_strategy: Union[str, float] = "warn",
      **kwargs: Any,
  ) -> None:
      super().__init__(
          "max",
          -torch.tensor(float("inf")),
          nan_strategy,
          state_name="max_value",
          **kwargs,
      )
  
imports: from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from torchmetrics.metric import Metric
  from torchmetrics.utilities import rank_zero_warn
  from torchmetrics.utilities.data import dim_zero_cat
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
  from torchmetrics.wrappers.running import Running
label_desc: Use image node_id 1 to create a new MaxMetric object from inherited base classes: BaseAggregator with example: obj = MaxMetric(nan_strategy)