code: def create_variables(
      network: snt.Module,
      input_spec: List[Union[types.NestedSpec, tf.TensorSpec]],
  ) -> Optional[tf.TensorSpec]:
      """Builds the network with dummy inputs to create the necessary variables.
  
      Args:
        network: Sonnet Module whose variables are to be created.
        input_spec: list of input specs to the network. The length of this list
          should match the number of arguments expected by `network`.
  
      Returns:
        output_spec: only returns an output spec if the output is a tf.Tensor, else
            it doesn't return anything (None); e.g. if the output is a
            tfp.distributions.Distribution.
      """
      # Create a dummy observation with no batch dimension.
      dummy_input = zeros_like(input_spec)
  
      # If we have an RNNCore the hidden state will be an additional input.
      if isinstance(network, snt.RNNCore):
          initial_state = squeeze_batch_dim(network.initial_state(1))
          dummy_input += [initial_state]
  
      # Forward pass of the network which will create variables as a side effect.
      dummy_output = network(*add_batch_dim(dummy_input))
  
      # Evaluate the input signature by converting the dummy input into a
      # TensorSpec. We then save the signature as a property of the network. This is
      # done so that we can later use it when creating snapshots. We do this here
      # because the snapshot code may not have access to the precise form of the
      # inputs.
      input_signature = tree.map_structure(
          lambda t: tf.TensorSpec((None,) + t.shape, t.dtype),
          dummy_input,
      )
      network._input_signature = (
          input_signature  # pylint: disable=protected-access
      )
  
      def spec(output):
          # If the output is not a Tensor, return None as spec is ill-defined.
          if not isinstance(output, tf.Tensor):
              return None
          # If this is not a scalar Tensor, make sure to squeeze out the batch dim.
          if tf.rank(output) > 0:
              output = squeeze_batch_dim(output)
          return tf.TensorSpec(output.shape, output.dtype)
  
      return tree.map_structure(spec, dummy_output)
  
imports: import functools
  from typing import List, Optional, Union
  from acme import types
  from acme.utils import tree_utils
  import sonnet
  import tensorflow
  import tree
label_desc: Use image node_id 7 for calling a global function with example usage: create_variables(network, input_spec) and returns: tree, tf, None