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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 array_to_base64_png(array):
 """Convert an array into base64-enoded PNG image. Args: array: A 2D np.ndarray or nested list of items. Returns: A base64-encoded string the image. The image is grayscale if the array is 2D. The image is RGB color if the image is 3D with lsat dimension equal to 3. Raises: ValueError: If the input `array` is not rank-2, or if the rank-2 `array` is empty. """

  # TODO(cais): Deal with 3D case.
  # TODO(cais): If there are None values in here, replace them with all NaNs.
  array = np.array(array, dtype=np.float32)
  if len(array.shape) != 2:
    raise ValueError(
        "Expected rank-2 array; received rank-%d array." % len(array.shape))
  if not np.size(array):
    raise ValueError(
        "Cannot encode an empty array (size: %s) as image." % (array.shape,))

  is_infinity = np.isinf(array)
  is_positive = array > 0.0
  is_positive_infinity = np.logical_and(is_infinity, is_positive)
  is_negative_infinity = np.logical_and(is_infinity,
                                        np.logical_not(is_positive))
  is_nan = np.isnan(array)
  finite_indices = np.where(np.logical_and(np.logical_not(is_infinity),
                                           np.logical_not(is_nan)))
  if np.size(finite_indices):
    # Finite subset is not empty.
    minval = np.min(array[finite_indices])
    maxval = np.max(array[finite_indices])
    scaled = np.array((array - minval) / (maxval - minval) * 255,
                      dtype=np.uint8)
    rgb = np.repeat(np.expand_dims(scaled, -1), IMAGE_COLOR_CHANNELS, axis=-1)
  else:
    rgb = np.zeros(array.shape + (IMAGE_COLOR_CHANNELS,), dtype=np.uint8)

  # Color-code pixels that correspond to infinities and nans.
  rgb[is_positive_infinity] = POSITIVE_INFINITY_RGB
  rgb[is_negative_infinity] = NEGATIVE_INFINITY_RGB
  rgb[is_nan] = NAN_RGB

  image_encoded = base64.b64encode(encoder.encode_png(rgb))
  return image_encoded