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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 _check_module_is_image_embedding(module_spec):
 """Raises ValueError if `module_spec` is not usable as image embedding. Args: module_spec: A `_ModuleSpec` to test. Raises: ValueError: if `module_spec` default signature is not compatible with mappingan "images" input to a Tensor(float32, shape=(_,K)). """

  issues = []

  # Find issues with "default" signature inputs. The common signatures for
  # image models prescribe a specific name; we trust it if we find it
  # and if we can do the necessary inference of input shapes from it.
  input_info_dict = module_spec.get_input_info_dict()
  if (list(input_info_dict.keys()) != ["images"] or
      input_info_dict["images"].dtype != tf.float32):
    issues.append("Module 'default' signature must require a single input, "
                  "which must have type float32 and name 'images'.")
  else:
    try:
      image_util.get_expected_image_size(module_spec)
    except ValueError as e:
      issues.append("Module does not support hub.get_expected_image_size(); "
                    "original error was:\n" + str(e))  # Raised again below.

  # Find issues with "default" signature outputs. We test that the dtype and
  # shape is appropriate for use in input_layer().
  output_info_dict = module_spec.get_output_info_dict()
  if "default" not in output_info_dict:
    issues.append("Module 'default' signature must have a 'default' output.")
  else:
    output_type = output_info_dict["default"].dtype
    output_shape = output_info_dict["default"].get_shape()
    if not (output_type == tf.float32 and output_shape.ndims == 2 and
            output_shape.dims[1].value):
      issues.append("Module 'default' signature must have a 'default' output "
                    "of tf.Tensor(shape=(_,K), dtype=float32).")

  if issues:
    raise ValueError("Module is not usable as image embedding: %r" % issues)