class_docstr: A model containing a CNN with N Conv2D layers.
  
  All layers share the same activation function, bias setup (use bias or not), and
  LayerNormalization setup (use layer normalization or not).
  
  Note that there is no flattening nor an additional dense layer at the end of the
  stack. The output of the network is a 3D tensor of dimensions [width x height x num
  output filters].
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docstr: Initializes a TfCNN instance.
  
  Args:
      input_dims: The 3D input dimensions of the network (incoming image).
      cnn_filter_specifiers: A list in which each element is another (inner) list
          of either the following forms:
          `[number of channels/filters, kernel, stride]`
          OR:
          `[number of channels/filters, kernel, stride, padding]`, where `padding`
          can either be "same" or "valid".
          When using the first format w/o the `padding` specifier, `padding` is
          "same" by default. Also, `kernel` and `stride` may be provided either as
          single ints (square) or as a tuple/list of two ints (width- and height
          dimensions) for non-squared kernel/stride shapes.
          A good rule of thumb for constructing CNN stacks is:
          When using padding="same", the input "image" will be reduced in size by
          the factor `stride`, e.g. input=(84, 84, 3) stride=2 kernel=x
          padding="same" filters=16 -> output=(42, 42, 16).
          For example, if you would like to reduce an Atari image from its
          original (84, 84, 3) dimensions down to (6, 6, F), you can construct the
          following stack and reduce the w x h dimension of the image by 2 in each
          layer:
          [[16, 4, 2], [32, 4, 2], [64, 4, 2], [128, 4, 2]] -> output=(6, 6, 128)
      cnn_use_bias: Whether to use bias on all Conv2D layers.
      cnn_activation: The activation function to use after each Conv2D layer.
      cnn_use_layernorm: Whether to insert a LayerNormalization functionality
          in between each Conv2D layer's outputs and its activation.
      cnn_kernel_initializer: The initializer function or class to use for kernel
          initialization in the CNN layers. If `None` the default initializer of
          the respective CNN layer is used. Note, all initializers defined in
          `tf.keras.initializers` are allowed.
      cnn_kernel_initializer_config: Configuration to pass into the initializer
          defined in `cnn_kernel_initializer`.
      cnn_bias_initializer: The initializer function or class to use for bias
          initialization in the CNN layers. If `None` the default initializer of
          the respective CNN layer is used. Note, all initializers defined in
          `tf.keras.initializers` are allowed.
      cnn_bias_initializer_config: Configuration to pass into the initializer
          defined in `cnn_bias_initializer`.
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code: def __init__(
      self,
      *,
      input_dims: Union[List[int], Tuple[int]],
      cnn_filter_specifiers: List[List[Union[int, List]]],
      cnn_use_bias: bool = True,
      cnn_use_layernorm: bool = False,
      cnn_activation: Optional[str] = "relu",
      cnn_kernel_initializer: Optional[Union[str, Callable]] = None,
      cnn_kernel_initializer_config: Optional[Dict] = None,
      cnn_bias_initializer: Optional[Union[str, Callable]] = None,
      cnn_bias_initializer_config: Optional[Dict] = None,
  ):
      """Initializes a TfCNN instance.
  
      Args:
          input_dims: The 3D input dimensions of the network (incoming image).
          cnn_filter_specifiers: A list in which each element is another (inner) list
              of either the following forms:
              `[number of channels/filters, kernel, stride]`
              OR:
              `[number of channels/filters, kernel, stride, padding]`, where `padding`
              can either be "same" or "valid".
              When using the first format w/o the `padding` specifier, `padding` is
              "same" by default. Also, `kernel` and `stride` may be provided either as
              single ints (square) or as a tuple/list of two ints (width- and height
              dimensions) for non-squared kernel/stride shapes.
              A good rule of thumb for constructing CNN stacks is:
              When using padding="same", the input "image" will be reduced in size by
              the factor `stride`, e.g. input=(84, 84, 3) stride=2 kernel=x
              padding="same" filters=16 -> output=(42, 42, 16).
              For example, if you would like to reduce an Atari image from its
              original (84, 84, 3) dimensions down to (6, 6, F), you can construct the
              following stack and reduce the w x h dimension of the image by 2 in each
              layer:
              [[16, 4, 2], [32, 4, 2], [64, 4, 2], [128, 4, 2]] -> output=(6, 6, 128)
          cnn_use_bias: Whether to use bias on all Conv2D layers.
          cnn_activation: The activation function to use after each Conv2D layer.
          cnn_use_layernorm: Whether to insert a LayerNormalization functionality
              in between each Conv2D layer's outputs and its activation.
          cnn_kernel_initializer: The initializer function or class to use for kernel
              initialization in the CNN layers. If `None` the default initializer of
              the respective CNN layer is used. Note, all initializers defined in
              `tf.keras.initializers` are allowed.
          cnn_kernel_initializer_config: Configuration to pass into the initializer
              defined in `cnn_kernel_initializer`.
          cnn_bias_initializer: The initializer function or class to use for bias
              initialization in the CNN layers. If `None` the default initializer of
              the respective CNN layer is used. Note, all initializers defined in
              `tf.keras.initializers` are allowed.
          cnn_bias_initializer_config: Configuration to pass into the initializer
              defined in `cnn_bias_initializer`.
      """
      super().__init__()
  
      assert len(input_dims) == 3
  
      cnn_activation = get_activation_fn(
          cnn_activation, framework="tf2"
      )
      cnn_kernel_initializer = get_initializer_fn(
          cnn_kernel_initializer, framework="tf2"
      )
      cnn_bias_initializer = get_initializer_fn(
          cnn_bias_initializer, framework="tf2"
      )
  
      layers = []
  
      # Input layer.
      layers.append(tf.keras.layers.Input(shape=input_dims))
  
      for filter_specs in cnn_filter_specifiers:
          # Padding information not provided -> Use "same" as default.
          if len(filter_specs) == 3:
              num_filters, kernel_size, strides = filter_specs
              padding = "same"
          # Padding information provided.
          else:
              num_filters, kernel_size, strides, padding = filter_specs
  
          layers.append(
              tf.keras.layers.Conv2D(
                  filters=num_filters,
                  kernel_size=kernel_size,
                  strides=strides,
                  padding=padding,
                  use_bias=cnn_use_bias,
                  activation=None
                  if cnn_use_layernorm
                  else cnn_activation,
                  # Note, if the initializer is `None`, we want TensorFlow
                  # to use its default one. So we pass in `None`.
                  kernel_initializer=(
                      cnn_kernel_initializer(
                          **cnn_kernel_initializer_config
                      )
                      if cnn_kernel_initializer_config
                      else cnn_kernel_initializer
                  ),
                  bias_initializer=(
                      cnn_bias_initializer(
                          **cnn_bias_initializer_config
                      )
                      if cnn_bias_initializer_config
                      else cnn_bias_initializer
                  ),
              )
          )
          if cnn_use_layernorm:
              # Use epsilon=1e-5 here (instead of default 1e-3) to be unified with
              # torch. Need to normalize over all axes.
              layers.append(
                  tf.keras.layers.LayerNormalization(
                      axis=[-3, -2, -1], epsilon=1e-5
                  )
              )
              layers.append(tf.keras.layers.Activation(cnn_activation))
  
      # Create the final CNN network.
      self.cnn = tf.keras.Sequential(layers)
  
      self.expected_input_dtype = tf.float32
  
file_path: ray/rllib/core/models/tf/primitives.py
imports: from typing import Callable, Dict, List, Optional, Tuple, Union
  from ray.rllib.models.utils import get_activation_fn, get_initializer_fn
  from ray.rllib.utils.framework import try_import_tf
label_desc: Use image node_id 1 to create a new TfCNN object from inherited base classes: tf.keras with example: obj = TfCNN()