class_docstr: Discrete CNN Q Function.
  
  A Q network that estimates Q values of all possible discrete actions.
  It is constructed using a CNN followed by one or more fully-connected
  layers.
  
  Args:
      env_spec (EnvSpec): Environment specification.
      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.
docstr: Return Q-value(s).
  
  Args:
      observations (np.ndarray): observations of shape :math: `(N, O*)`.
  
  Returns:
      torch.Tensor: Output value
docstr_tok: ["Return","Q-value","(","s",")",".","Args",":","observations","(","np.ndarray",")",":","observations","of","shape",":","math",":","`","(","N",",","O","*",")","`",".","Returns",":","torch.Tensor",":","Output","value"]
code: def forward(self, observations):
      """Return Q-value(s).
  
      Args:
          observations (np.ndarray): observations of shape :math: `(N, O*)`.
  
      Returns:
          torch.Tensor: Output value
      """
      # We're given flattened observations.
      observations = observations.reshape(
          -1, *self._env_spec.observation_space.shape
      )
      return self._cnn_module(observations)
  
imports: import torch
  from torch import nn
  from garage import InOutSpec
  from garage.torch.modules import DiscreteCNNModule
label_desc: Use image node_id 2 for calling the DiscreteCNNQFunction obj's underlying member method code with example usage: obj.forward(observations) and returns: self