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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def image_embedding_column(key, module_spec):
 """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") height, width = hub.get_expected_image_size(image_column.module_spec) ``` 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. """

  module_spec = module.as_module_spec(module_spec)
  _check_module_is_image_embedding(module_spec)
  return _ImageEmbeddingColumn(key=key, module_spec=module_spec)