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# Normalize

class torchvision.transforms.Normalize(mean, std, inplace=False)

  Normalize a tensor image with mean and standard deviation. This transform does not support PIL Image. Given mean: `(mean[1],...,mean[n])` and std: `(std[1],..,std[n])` for `n` channels, this transform will normalize each channel of the input `torch.*Tensor` i.e., `output[channel] = (input[channel] - mean[channel]) / std[channel]`

  Note

  This transform acts out of place, i.e., it does not mutate the input tensor.

  Parameters:

    - mean (sequence) – Sequence of means for each channel.

    - std (sequence) – Sequence of standard deviations for each channel.

    - inplace (bool,optional) – Bool to make this operation in-place.

  forward(tensor: Tensor) → Tensor
    Parameters:

      tensor (Tensor) – Tensor image to be normalized.

    Returns:

      Normalized Tensor image.

    Return type:

      Tensor

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