class_docstr: Base class for encoders in TensorFlow.
class_docstr_tok: ["Base","class","for","encoders","in","TensorFlow","."]
docstr: Get a sample of embedding for the given input.
  
  Args:
      input_value (numpy.ndarray): Tensor to encode.
  
  Returns:
      numpy.ndarray: An embedding sampled from embedding distribution.
      dict: Embedding distribution information.
  
  Note:
      It returns an embedding and a dict, with keys
      - mean (numpy.ndarray): Mean of the distribution.
      - log_std (numpy.ndarray): Log standard deviation of the
          distribution.
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code: def get_latent(self, input_value):
      """Get a sample of embedding for the given input.
  
      Args:
          input_value (numpy.ndarray): Tensor to encode.
  
      Returns:
          numpy.ndarray: An embedding sampled from embedding distribution.
          dict: Embedding distribution information.
  
      Note:
          It returns an embedding and a dict, with keys
          - mean (numpy.ndarray): Mean of the distribution.
          - log_std (numpy.ndarray): Log standard deviation of the
              distribution.
  
      """
  
file_path: garage/src/garage/tf/embeddings/encoder.py
imports: from garage.np.embeddings import Encoder
  from garage.np.embeddings import StochasticEncoder
  from garage.tf.models import Module, StochasticModule
label_desc: Use image node_id 1 for calling the Encoder obj's underlying member method code with example usage: obj.get_latent(input_value) without return types