class_docstr: Implements an intermediate layer getter
  
  >>> from tensorflow.keras.applications import ResNet50
  >>> from doctr.models import IntermediateLayerGetter
  >>> target_layers = ["conv2_block3_out", "conv3_block4_out", "conv4_block6_out", "conv5_block3_out"]
  >>> feat_extractor = IntermediateLayerGetter(ResNet50(include_top=False, pooling=False), target_layers)
  
  Args:
  ----
      model: the model to extract feature maps from
      layer_names: the list of layers to retrieve the feature map from
class_docstr_tok: ["Implements","an","intermediate","layer","getter",">",">",">","from","tensorflow.keras.applications","import","ResNet50",">",">",">","from","doctr.models","import","IntermediateLayerGetter",">",">",">","target_layers","=","[","``","conv2_block3_out","''",",","``","conv3_block4_out","''",",","``","conv4_block6_out","''",",","``","conv5_block3_out","''","]",">",">",">","feat_extractor","=","IntermediateLayerGetter","(","ResNet50","(","include_top=False",",","pooling=False",")",",","target_layers",")","Args",":","--","--","model",":","the","model","to","extract","feature","maps","from","layer_names",":","the","list","of","layers","to","retrieve","the","feature","map","from"]
code: def __init__(self, model: Model, layer_names: List[str]) -> None:
      intermediate_fmaps = [
          model.get_layer(layer_name).get_output_at(0)
          for layer_name in layer_names
      ]
      super().__init__(model.input, outputs=intermediate_fmaps)
  
file_path: doctr/doctr/models/utils/tensorflow.py
imports: import logging
  import os
  from typing import Any, Callable, List, Optional, Tuple, Union
  from zipfile import ZipFile
  import tensorflow
  import tf2onnx
  from tensorflow.keras import Model, layers
  from doctr.utils.data import download_from_url
label_desc: Use image node_id 1 to create a new IntermediateLayerGetter object from inherited base classes: Model with example: obj = IntermediateLayerGetter(model, layer_names)