class_docstr: Instantiates the ResNet50 architecture.
  
  Args:
    data_format: format for the image. Either 'channels_first' or
      'channels_last'.  'channels_first' is typically faster on GPUs while
      'channels_last' is typically faster on CPUs. See
      https://www.tensorflow.org/performance/performance_guide#data_formats
    name: Prefix applied to names of variables created in the model.
    trainable: Is the model trainable? If true, performs backward
        and optimization after call() method.
    include_top: whether to include the fully-connected layer at the top of the
      network.
    pooling: Optional pooling mode for feature extraction when `include_top`
      is `False`.
      - `None` means that the output of the model will be the 4D tensor
          output of the last convolutional layer.
      - `avg` means that global average pooling will be applied to the output of
          the last convolutional layer, and thus the output of the model will be
          a 2D tensor.
      - `max` means that global max pooling will be applied.
    block3_strides: whether to add a stride of 2 to block3 to make it compatible
      with tf.slim ResNet implementation.
    average_pooling: whether to do average pooling of block4 features before
      global pooling.
    classes: optional number of classes to classify images into, only to be
      specified if `include_top` is True.
  
  Raises:
      ValueError: in case of invalid argument for data_format.
docstr: Call the ResNet50 model.
  
  Args:
    inputs: Images to compute features for.
    training: Whether model is in training phase.
    intermediates_dict: `None` or dictionary. If not None, accumulate feature
      maps from intermediate blocks into the dictionary.
      ""
  
  Returns:
    Tensor with featuremap.
docstr_tok: ["Call","the","ResNet50","model",".","Args",":","inputs",":","Images","to","compute","features","for",".","training",":","Whether","model","is","in","training","phase",".","intermediates_dict",":","`","None","`","or","dictionary",".","If","not","None",",","accumulate","feature","maps","from","intermediate","blocks","into","the","dictionary.","``","''","Returns",":","Tensor","with","featuremap","."]
code: def call(self, inputs, training=True, intermediates_dict=None):
      """Call the ResNet50 model.
  
      Args:
        inputs: Images to compute features for.
        training: Whether model is in training phase.
        intermediates_dict: `None` or dictionary. If not None, accumulate feature
          maps from intermediate blocks into the dictionary.
          ""
  
      Returns:
        Tensor with featuremap.
      """
  
      x = self.conv1(inputs)
      x = self.bn_conv1(x, training=training)
      x = tf.nn.relu(x)
      if intermediates_dict is not None:
          intermediates_dict["block0"] = x
  
      x = self.max_pool(x)
      if intermediates_dict is not None:
          intermediates_dict["block0mp"] = x
  
      # Block 1 (equivalent to "conv2" in Resnet paper).
      x = self.l2a(x, training=training)
      x = self.l2b(x, training=training)
      x = self.l2c(x, training=training)
      if intermediates_dict is not None:
          intermediates_dict["block1"] = x
  
      # Block 2 (equivalent to "conv3" in Resnet paper).
      x = self.l3a(x, training=training)
      x = self.l3b(x, training=training)
      x = self.l3c(x, training=training)
      x = self.l3d(x, training=training)
      if intermediates_dict is not None:
          intermediates_dict["block2"] = x
  
      # Block 3 (equivalent to "conv4" in Resnet paper).
      x = self.l4a(x, training=training)
      x = self.l4b(x, training=training)
      x = self.l4c(x, training=training)
      x = self.l4d(x, training=training)
      x = self.l4e(x, training=training)
      x = self.l4f(x, training=training)
  
      if self.block3_strides:
          x = self.subsampling_layer(x)
          if intermediates_dict is not None:
              intermediates_dict["block3"] = x
      else:
          if intermediates_dict is not None:
              intermediates_dict["block3"] = x
  
      x = self.l5a(x, training=training)
      x = self.l5b(x, training=training)
      x = self.l5c(x, training=training)
  
      if self.average_pooling:
          x = self.avg_pool(x)
          if intermediates_dict is not None:
              intermediates_dict["block4"] = x
      else:
          if intermediates_dict is not None:
              intermediates_dict["block4"] = x
  
      if self.include_top:
          return self.fc1000(self.flatten(x))
      elif self.global_pooling:
          return self.global_pooling(x)
      else:
          return x
  
file_path: tensorflow/tensorflow/python/eager/benchmarks/resnet50/resnet50.py
imports: import functools
  import tensorflow
label_desc: Use image node_id 2 for calling the ResNet50 obj's underlying member method code with example usage: obj.call(inputs, training, intermediates_dict) and returns: self, self, x