class_docstr: This is a abstract context-aware recommender. All the context-aware model should implement this class.
  The base context-aware recommender class provide the basic embedding function of feature fields which also
  contains a first-order part of feature fields.
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docstr: Embed the float feature columns
  
  Args:
      float_fields (torch.FloatTensor): The input dense tensor. shape of [batch_size, num_float_field]
  
  Returns:
      torch.FloatTensor: The result embedding tensor of float columns.
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code: def embed_float_fields(self, float_fields):
      """Embed the float feature columns
  
      Args:
          float_fields (torch.FloatTensor): The input dense tensor. shape of [batch_size, num_float_field]
  
      Returns:
          torch.FloatTensor: The result embedding tensor of float columns.
      """
      # input Tensor shape : [batch_size, num_float_field]
      if float_fields is None:
          return None
      # [batch_size, num_float_field, embed_dim]
      float_embedding = self.float_embedding_table(float_fields)
  
      return float_embedding
  
imports: from logging import getLogger
  import numpy
  import torch
  import torch.nn
  from recbole.model.layers import FMEmbedding, FMFirstOrderLinear, FLEmbedding
  from recbole.utils import ModelType, InputType, FeatureSource, FeatureType, set_color
label_desc: Use image node_id 2 for calling the ContextRecommender obj's underlying member method code with example usage: obj.embed_float_fields(float_fields) and returns: float_embedding, None