class_docstr: A fully connected network that takes FloatFeatures input
  and supports distributional prediction.
code: def forward(
      self,
      state: rlt.FeatureData,
  ) -> torch.Tensor:
      float_features = state.float_features
      x = self.fc(float_features)
      if self.num_atoms is not None:
          x = x.view(
              float_features.shape[0], self.action_dim, self.num_atoms
          )
      return x
  
imports: import logging
  import math
  from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.init
  from reagent.core import types
  from reagent.models.base import ModelBase
  from reagent.models.residual_wrapper import ResidualWrapper
label_desc: Use image node_id 3 for calling the FloatFeatureFullyConnected obj's underlying member method code with example usage: obj.forward(state) and returns: x