code: def image_embedding_column(key, module_spec):
    
    module_spec = module.as_module_spec(module_spec)
    _check_module_is_image_embedding(module_spec)
    return _ImageEmbeddingColumn(key=key, module_spec=module_spec)
docs: Uses a Module to get a dense 1-D representation from the pixels of images.
  
    This feature column can be used on images, represented as float32 tensors of
    RGB pixel data in the range [0,1]. This can be read from a numeric_column()
    if the tf.Example input data happens to have decoded images, all with the
    same shape [height, width, 3]. More commonly, the input_fn will have code to
    explicitly decode images, resize them (possibly after performing data
    augmentation such as random crops etc.), and provide a batch of shape
    [batch_size, height, width, 3].
  
    The result of this feature column is the result of passing its `input`
    through the module `m` instantiated from `module_spec`, as per
    `result = m({"images": input})`. The `result` must have dtype float32 and
    shape `[batch_size, num_features]` with a known value of num_features.
  
    Example:
  
    ```python
      image_column = hub.image_embedding_column("embeddings", "/tmp/image-module")
      feature_columns = [image_column, ...]
      estimator = tf.estimator.LinearClassifier(feature_columns, ...)
      height, width = hub.get_expected_image_size(image_column.module_spec)
      input_fn = ...  # Provides "embeddings" with shape [None, height, width, 3].
      estimator.train(input_fn, ...)
    ```
  
    Args:
      key: A string or `_FeatureColumn` identifying the input image data.
      module_spec: A string handle or a `ModuleSpec` identifying the module.
  
    Returns:
      `_DenseColumn` that converts from pixel data.
  
    Raises:
       ValueError: if module_spec is not suitable for use in this feature column.