class_docstr: **Mean Square Error Loss**
  Computes the mean square error (squared L2 norm) of given input and label.
  If :attr:`reduction` is set to ``'none'``, loss is calculated as:
  .. math::
      Out = (input - label)^2
  If :attr:`reduction` is set to ``'mean'``, loss is calculated as:
  .. math::
      Out = \operatorname{mean}((input - label)^2)
  If :attr:`reduction` is set to ``'sum'``, loss is calculated as:
  .. math::
      Out = \operatorname{sum}((input - label)^2)
  where `input` and `label` are `float32` tensors of same shape.
  
  Args:
      reduction (string, optional): The reduction method for the output,
          could be 'none' | 'mean' | 'sum'.
          If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned.
          If :attr:`size_average` is ``'sum'``, the reduced sum loss is returned.
          If :attr:`reduction` is ``'none'``, the unreduced loss is returned.
          Default is ``'mean'``.
      ignore_index (int, optional): Specifies a target value that is ignored and does not contribute to the input gradient. Default: 255.
  Shape:
      input (Tensor): Input tensor, the data type is float32 or float64
      label (Tensor): Label tensor, the data type is float32 or float64
      output (Tensor): output tensor storing the MSE loss of input and label, the data type is same as input.
  Examples:
      .. code-block:: python
          import numpy as np
          import paddle
          input_data = np.array([1.5]).astype("float32")
          label_data = np.array([1.7]).astype("float32")
          mse_loss = paddle.nn.loss.MSELoss()
          input = paddle.to_tensor(input_data)
          label = paddle.to_tensor(label_data)
          output = mse_loss(input, label)
          print(output)
          # [0.04000002]
code: def __init__(self, reduction="mean", ignore_index=255):
      super().__init__(reduction=reduction)
  
imports: import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 1 to create a new MSELoss object from inherited base classes: nn with example: obj = MSELoss(reduction, ignore_index)