code: def __init__(
      self,
      activation_function: str = "relu",
      scheme: Union[List, EmbedderScheme] = EmbedderScheme.Medium,
      batchnorm: bool = False,
      dropout_rate: float = 0.0,
      name: str = "embedder",
      input_rescaling=None,
      input_offset=None,
      input_clipping=None,
      dense_layer=None,
      is_training=False,
      flatten=True,
  ):
      super().__init__(dense_layer=dense_layer)
      self.activation_function = activation_function
      self.scheme = scheme
      self.batchnorm = batchnorm
      self.dropout_rate = dropout_rate
  
      if input_rescaling is None:
          input_rescaling = {
              "image": 255.0,
              "vector": 1.0,
              "tensor": 1.0,
          }
      if input_offset is None:
          input_offset = {"image": 0.0, "vector": 0.0, "tensor": 0.0}
  
      self.input_rescaling = input_rescaling
      self.input_offset = input_offset
      self.input_clipping = input_clipping
      self.name = name
      self.is_training = is_training
      self.flatten = flatten
  
imports: from typing import List, Union
  from rl_coach.base_parameters import EmbedderScheme, NetworkComponentParameters
label_desc: Use image node_id 1 to create a new InputEmbedderParameters object from inherited base classes: NetworkComponentParameters with example: obj = InputEmbedderParameters(activation_function, scheme, batchnorm, dropout_rate, name, input_rescaling, input_offset, input_clipping, dense_layer, is_training, flatten)