class_docstr: Compute Root Mean Squared Error between two tensors using function:
  
  .. math::
      \operatorname {RMSE}\left(Y, \hat{Y}\right) ={ \sqrt{ \frac {1}{n}\sum _{i=1}^{n}\left(y_i-\hat{y_i}\right)^2 } } \
      = \sqrt {\operatorname{MSE}\left(Y, \hat{Y}\right)}.
  
  More info: https://en.wikipedia.org/wiki/Root-mean-square_deviation
  
  Input `y_pred` is compared with ground truth `y`.
  Both `y_pred` and `y` are expected to be real-valued, where `y_pred` is output from a regression model.
  
  Example of the typical execution steps of this metric class follows :py:class:`monai.metrics.metric.Cumulative`.
  
  Args:
      reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,
          available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
          ``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
      get_not_nans: whether to return the `not_nans` count, if True, aggregate() returns (metric, not_nans).
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code: def _compute_metric(
      self, y_pred: torch.Tensor, y: torch.Tensor
  ) -> torch.Tensor:
      mse_out = compute_mean_error_metrics(y_pred, y, func=self.sq_func)
      return torch.sqrt(mse_out)
  
imports: from __future__ import annotations
  import math
  from abc import abstractmethod
  from collections.abc import Callable, Sequence
  from functools import partial
  from typing import Any
  import torch
  import torch.nn.functional
  from monai.metrics.utils import do_metric_reduction
  from monai.utils import MetricReduction, StrEnum, convert_data_type, ensure_tuple_rep
  from monai.utils.type_conversion import convert_to_dst_type
  from .metric import CumulativeIterationMetric
label_desc: Use image node_id 2 for calling the RMSEMetric obj's underlying member method code with example usage: obj._compute_metric(y_pred, y) and returns: torch