class_docstr: Variant B of the three InceptionResNet layers described in
  https://arxiv.org/abs/1710.02238. All variants use multiple
  convolutional blocks with varying kernel sizes and number of filters. This
  allows capturing patterns over different scales in the inputs. Residual
  connections are additionally used and have been shown previously to improve
  convergence and training in deep networks. A 1x1 convolution is used on the
  concatenated feature maps from the different convolutional blocks, to ensure
  shapes of inputs and feature maps are same for the residual connection.
docstr: Parameters
  ----------
  num_filters: int,
      Number of convolutional filters
  input_dim: int,
      Number of channels in the input.
docstr_tok: ["Parameters","--","--","--","--","--","num_filters",":","int",",","Number","of","convolutional","filters","input_dim",":","int",",","Number","of","channels","in","the","input","."]
code: def __init__(self, num_filters, input_dim, **kwargs):
      """
      Parameters
      ----------
      num_filters: int,
          Number of convolutional filters
      input_dim: int,
          Number of channels in the input.
      """
      self.num_filters = num_filters
      self.input_dim = input_dim
      self._build_layer_components()
      super(InceptionResnetB, self).__init__(**kwargs)
  
imports: import tensorflow
  from tensorflow.keras.layers import Conv2D, Concatenate, ReLU, Add, MaxPool2D
label_desc: Use image node_id 1 to create a new InceptionResnetB object from inherited base classes: tf.keras.layers with example: obj = InceptionResnetB(num_filters, input_dim)