class_docstr: Interface for a ranking model.
class_docstr_tok: ["Interface","for","a","ranking","model","."]
docstr: Implements the logic that computes the logits from input dense Tensors.
  
  Args:
    context_features: (dict) A dict of dense Tensors for context features.
      Each Tensor is 2-D and has a shape of [batch_size, feature_size].
    example_features: (dict) A dict of dense Tensors for example features.
      Each Tensor id 3-D and has a shape of [batch_size, list_size,
      feature_size].
    labels: See `compute_logits`.
    mode: See `compute_logits`.
    params: See `compute_logits`.
    config: See `compute_logits`.
  
  Returns:
    A Tensor representing the logits.
docstr_tok: ["Implements","the","logic","that","computes","the","logits","from","input","dense","Tensors",".","Args",":","context_features",":","(","dict",")","A","dict","of","dense","Tensors","for","context","features",".","Each","Tensor","is","2-D","and","has","a","shape","of","[","batch_size",",","feature_size","]",".","example_features",":","(","dict",")","A","dict","of","dense","Tensors","for","example","features",".","Each","Tensor","id","3-D","and","has","a","shape","of","[","batch_size",",","list_size",",","feature_size","]",".","labels",":","See","`","compute_logits","`",".","mode",":","See","`","compute_logits","`",".","params",":","See","`","compute_logits","`",".","config",":","See","`","compute_logits","`",".","Returns",":","A","Tensor","representing","the","logits","."]
code: def _compute_logits_impl(
      self,
      context_features,
      example_features,
      labels,
      mode,
      params,
      config,
  ):
      """Implements the logic that computes the logits from input dense Tensors.
  
      Args:
        context_features: (dict) A dict of dense Tensors for context features.
          Each Tensor is 2-D and has a shape of [batch_size, feature_size].
        example_features: (dict) A dict of dense Tensors for example features.
          Each Tensor id 3-D and has a shape of [batch_size, list_size,
          feature_size].
        labels: See `compute_logits`.
        mode: See `compute_logits`.
        params: See `compute_logits`.
        config: See `compute_logits`.
  
      Returns:
        A Tensor representing the logits.
      """
      raise NotImplementedError("Calling an abstract method.")
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import six
  import tensorflow
  from tensorflow import estimator
  from tensorflow.python.util import function_utils
  from tensorflow_ranking.python import feature
  from tensorflow_ranking.python import utils
label_desc: Use image node_id 4 for calling the _RankingModel obj's underlying member method code with example usage: obj._compute_logits_impl(context_features, example_features, labels, mode, params, config) without return types