class_docstr: Wrapper for architecture configurations.
  
  This class provides access to configurations for the architecture used in the
  experiment.
  
  Attributes
    embed_dim: Dimension of the feature representation of the architecture.
    input_dim: Dimension of the input of the architecture.
    model_link: Link to the model in tfhub.
code: def __init__(self, model_type: str):
      self.input_dim = (224, 224, 3)
      if model_type == "resnet50":
          self.embed_dim = 2048
          self.model_link = "https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/5"
      elif model_type == "resnet101":
          self.embed_dim = 2048
          self.model_link = "https://tfhub.dev/google/imagenet/resnet_v2_101/feature_vector/5"
      elif model_type == "resnet152":
          self.embed_dim = 2048
          self.model_link = "https://tfhub.dev/google/imagenet/resnet_v2_152/feature_vector/5"
      elif model_type == "bit-m152x4":
          self.embed_dim = 8192
          self.model_link = "https://tfhub.dev/google/bit/m-r152x4/1"
      elif model_type == "vitb8_clf":
          self.embed_dim = None  # Classifier
          self.model_link = (
              "https://tfhub.dev/sayakpaul/vit_b8_classification/1"
          )
      else:
          raise ValueError(
              "Model type not understood: {}".format(model_type)
          )
  
imports: from meo.mlp_baseline import data_utils
label_desc: Use image node_id 1 to create a new ModelConfig object from inherited base classes: object with example: obj = ModelConfig(model_type)