code: def __init__(
      self,
      input_dims,
      max_freq,
      N_freqs,
      log_sampling=True,
      include_input=True,
      min_freq=0,
      mapping="posenc",
  ):
      super().__init__()
      self.input_dims = input_dims
      self.max_freq = max_freq
      self.min_freq = min_freq
      self.N_freqs = N_freqs
      self.log_sampling = log_sampling
      self.include_input = include_input
      self.out_dim = 0
      self.mapping = mapping
      if mapping == "rotate":
          self.create_rotated_embedding()
      elif mapping == "posenc":
          self.create_embedding_fn()
      else:
          assert ValueError(mapping)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import numpy
label_desc: Use image node_id 1 to create a new Embedder object from inherited base classes: nn with example: obj = Embedder(input_dims, max_freq, N_freqs, log_sampling, include_input, min_freq, mapping)