class_docstr: ImNet layer keras implementation.
    
docstr: Initialization.
  
  Args:
    dim: int, dimension of input points.
    in_features: int, length of input features (i.e., latent code).
    out_features: number of output features.
    num_filters: int, width of the second to last layer.
    activation: tf activation op.
    name: str, name of the layer.
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code: def __init__(
      self,
      dim=3,
      in_features=128,
      out_features=1,
      num_filters=128,
      activation=tf.nn.leaky_relu,
      name="im_net",
  ):
      """Initialization.
  
      Args:
        dim: int, dimension of input points.
        in_features: int, length of input features (i.e., latent code).
        out_features: number of output features.
        num_filters: int, width of the second to last layer.
        activation: tf activation op.
        name: str, name of the layer.
      """
      super(ImNet, self).__init__(name=name)
      self.dim = dim
      self.in_features = in_features
      self.dimz = dim + in_features
      self.out_features = out_features
      self.num_filters = num_filters
      self.activ = activation
      self.fc0 = layers.Dense(num_filters * 16, name="dense_1")
      self.fc1 = layers.Dense(num_filters * 8, name="dense_2")
      self.fc2 = layers.Dense(num_filters * 4, name="dense_3")
      self.fc3 = layers.Dense(num_filters * 2, name="dense_4")
      self.fc4 = layers.Dense(num_filters * 1, name="dense_5")
      self.fc5 = layers.Dense(out_features, name="dense_6")
      self.fc = [
          self.fc0,
          self.fc1,
          self.fc2,
          self.fc3,
          self.fc4,
          self.fc5,
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new ImNet object from inherited base classes: layers with example: obj = ImNet(dim, in_features, out_features, num_filters, activation, name)