class_docstr: Discrete CNN Module.
  
  A CNN followed by one or more fully connected layers with a set number
  of discrete outputs.
  
  Args:
      spec (garage.InOutSpec): Specification of inputs and outputs.
          The input should be in 'NCHW' format: [batch_size, channel, height,
          width]. Will print a warning if the channel size is not 1 or 3.
          The output space will be flattened.
      image_format (str): Either 'NCHW' or 'NHWC'. Should match the input
          specification. Gym uses NHWC by default, but PyTorch uses NCHW by
          default.
      kernel_sizes (tuple[int]): Dimension of the conv filters.
          For example, (3, 5) means there are two convolutional layers.
          The filter for first layer is of dimension (3 x 3)
          and the second one is of dimension (5 x 5).
      strides (tuple[int]): The stride of the sliding window. For example,
          (1, 2) means there are two convolutional layers. The stride of the
          filter for first layer is 1 and that of the second layer is 2.
      hidden_channels (tuple[int]): Number of output channels for CNN.
          For example, (3, 32) means there are two convolutional layers.
          The filter for the first conv layer outputs 3 channels
          and the second one outputs 32 channels.
      hidden_sizes (list[int]): Output dimension of dense layer(s) for
          the MLP for mean. For example, (32, 32) means the MLP consists
          of two hidden layers, each with 32 hidden units.
      mlp_hidden_nonlinearity (callable): Activation function for
          intermediate dense layer(s) in the MLP. It should return
          a torch.Tensor. Set it to None to maintain a linear activation.
      cnn_hidden_nonlinearity (callable): Activation function for
          intermediate CNN layer(s). It should return a torch.Tensor.
          Set it to None to maintain a linear activation.
      hidden_w_init (callable): Initializer function for the weight
          of intermediate dense layer(s). The function should return a
          torch.Tensor.
      hidden_b_init (callable): Initializer function for the bias
          of intermediate dense layer(s). The function should return a
          torch.Tensor.
      paddings (tuple[int]):  Zero-padding added to both sides of the input
      padding_mode (str): The type of padding algorithm to use,
          either 'SAME' or 'VALID'.
      max_pool (bool): Bool for using max-pooling or not.
      pool_shape (tuple[int]): Dimension of the pooling layer(s). For
          example, (2, 2) means that all the pooling layers are of the same
          shape (2, 2).
      pool_stride (tuple[int]): The strides of the pooling layer(s). For
          example, (2, 2) means that all the pooling layers have
          strides (2, 2).
      output_nonlinearity (callable): Activation function for output dense
          layer. It should return a torch.Tensor. Set it to None to
          maintain a linear activation.
      output_w_init (callable): Initializer function for the weight
          of output dense layer(s). The function should return a
          torch.Tensor.
      output_b_init (callable): Initializer function for the bias
          of output dense layer(s). The function should return a
          torch.Tensor.
      layer_normalization (bool): Bool for using layer normalization or not.
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docstr: Forward method.
  
  Args:
      inputs (torch.Tensor): Inputs to the model of shape
          (input_shape*).
  
  Returns:
      torch.Tensor: Output tensor of shape :math:`(N, output_dim)`.
docstr_tok: ["Forward","method",".","Args",":","inputs","(","torch.Tensor",")",":","Inputs","to","the","model","of","shape","(","input_shape","*",")",".","Returns",":","torch.Tensor",":","Output","tensor","of","shape",":","math",":","`","(","N",",","output_dim",")","`","."]
code: def forward(self, inputs):
      """Forward method.
  
      Args:
          inputs (torch.Tensor): Inputs to the model of shape
              (input_shape*).
  
      Returns:
          torch.Tensor: Output tensor of shape :math:`(N, output_dim)`.
  
      """
      return self._module(inputs)
  
imports: from torch import nn
  from garage import InOutSpec
  from garage.torch.modules import CNNModule, MLPModule
label_desc: Use image node_id 2 for calling the DiscreteCNNModule obj's underlying member method code with example usage: obj.forward(inputs) and returns: self