class_docstr: Compute the `Cosine Similarity`_.
  
  .. math::
      cos_{sim}(x,y) = \frac{x \cdot y}{||x|| \cdot ||y||} =
      \frac{\sum_{i=1}^n x_i y_i}{\sqrt{\sum_{i=1}^n x_i^2}\sqrt{\sum_{i=1}^n y_i^2}}
  
  where :math:`y` is a tensor of target values, and :math:`x` is a tensor of predictions.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): Predicted float tensor with shape ``(N,d)``
  - ``target`` (:class:`~torch.Tensor`): Ground truth float tensor with shape ``(N,d)``
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``cosine_similarity`` (:class:`~torch.Tensor`): A float tensor with the cosine similarity
  
  Args:
      reduction: how to reduce over the batch dimension using 'sum', 'mean' or 'none' (taking the individual scores)
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> from torch import tensor
      >>> from torchmetrics.regression import CosineSimilarity
      >>> target = tensor([[0, 1], [1, 1]])
      >>> preds = tensor([[0, 1], [0, 1]])
      >>> cosine_similarity = CosineSimilarity(reduction = 'mean')
      >>> cosine_similarity(preds, target)
      tensor(0.8536)
code: def __init__(
      self,
      reduction: Literal["mean", "sum", "none", None] = "sum",
      **kwargs: Any,
  ) -> None:
      super().__init__(**kwargs)
      allowed_reduction = ("sum", "mean", "none", None)
      if reduction not in allowed_reduction:
          raise ValueError(
              f"Expected argument `reduction` to be one of {allowed_reduction} but got {reduction}"
          )
      self.reduction = reduction
  
      self.add_state("preds", [], dist_reduce_fx="cat")
      self.add_state("target", [], dist_reduce_fx="cat")
  
file_path: torchmetrics/src/torchmetrics/regression/cosine_similarity.py
imports: from typing import Any, List, Optional, Sequence, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.regression.cosine_similarity import _cosine_similarity_compute, _cosine_similarity_update
  from torchmetrics.metric import Metric
  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
label_desc: Use image node_id 1 to create a new CosineSimilarity object from inherited base classes: Metric with example: obj = CosineSimilarity(reduction)