class_docstr: An input embedder that performs convolutions on the input and then flattens the result.
  The embedder is intended for image like inputs, where the channels are expected to be the last axis.
  The embedder also allows custom rescaling of the input prior to the neural network.
code: def __init__(
      self,
      input_size: List[int],
      activation_function=tf.nn.relu,
      scheme: EmbedderScheme = EmbedderScheme.Medium,
      batchnorm: bool = False,
      dropout_rate: float = 0.0,
      name: str = "embedder",
      input_rescaling: float = 255.0,
      input_offset: float = 0.0,
      input_clipping=None,
      dense_layer=Dense,
      is_training=False,
      flatten=True,
  ):
      super().__init__(
          input_size,
          activation_function,
          scheme,
          batchnorm,
          dropout_rate,
          name,
          input_rescaling,
          input_offset,
          input_clipping,
          dense_layer=dense_layer,
          is_training=is_training,
          flatten=flatten,
      )
      self.return_type = InputImageEmbedding
      if len(input_size) != 3 and scheme != EmbedderScheme.Empty:
          raise ValueError(
              "Image embedders expect the input size to have 3 dimensions. The given size is: {}".format(
                  input_size
              )
          )
  
file_path: coach/rl_coach/architectures/tensorflow_components/embedders/image_embedder.py
imports: from typing import List
  import tensorflow
  from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense
  from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedder
  from rl_coach.base_parameters import EmbedderScheme
  from rl_coach.core_types import InputImageEmbedding
label_desc: Use image node_id 1 to create a new ImageEmbedder object from inherited base classes: InputEmbedder with example: obj = ImageEmbedder(input_size, activation_function, scheme, batchnorm, dropout_rate, name, input_rescaling, input_offset, input_clipping, dense_layer, is_training, flatten)