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# MLP

class torchvision.ops.MLP(in_channels: int, hidden_channels: ~typing.List[int], norm_layer: ~typing.Optional[~typing.Callable[[...], ~torch.nn.modules.module.Module]] = None, activation_layer: ~typing.Optional[~typing.Callable[[...], ~torch.nn.modules.module.Module]] = <class 'torch.nn.modules.activation.ReLU'>, inplace: ~typing.Optional[bool] = None, bias: bool = True, dropout: float = 0.0)

  This block implements the multi-layer perceptron (MLP) module.

  Parameters:

    - in_channels (int) – Number of channels of the input

    - hidden_channels (List[int]) – List of the hidden channel dimensions

    - norm_layer (Callable[..., torch.nn.Module], optional) – Norm layer that will be stacked on top of the linear layer. If `None` this layer won’t be used. Default: `None`

    - activation_layer (Callable[..., torch.nn.Module], optional) – Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the linear layer. If `None` this layer won’t be used. Default: `torch.nn.ReLU`

    - inplace (bool, optional) – Parameter for the activation layer, which can optionally do the operation in-place. Default is `None`, which uses the respective default values of the `activation_layer` and Dropout layer.

    - bias (bool) – Whether to use bias in the linear layer. Default `True`

    - dropout (float) – The probability for the dropout layer. Default: 0.0

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