code: def form_image_grid(
      input_tensor, grid_shape, image_shape, num_channels
  ):
      """Arrange a minibatch of images into a grid to form a single image.
  
      Args:
        input_tensor: Tensor. Minibatch of images to format, either 4D
            ([batch size, height, width, num_channels]) or flattened
            ([batch size, height * width * num_channels]).
        grid_shape: Sequence of int. The shape of the image grid,
            formatted as [grid_height, grid_width].
        image_shape: Sequence of int. The shape of a single image,
            formatted as [image_height, image_width].
        num_channels: int. The number of channels in an image.
  
      Returns:
        Tensor representing a single image in which the input images have been
        arranged into a grid.
  
      Raises:
        ValueError: The grid shape and minibatch size don't match, or the image
            shape and number of channels are incompatible with the input tensor.
      """
      if grid_shape[0] * grid_shape[1] != int(input_tensor.shape[0]):
          raise ValueError(
              "Grid shape incompatible with minibatch size."
          )
      if len(input_tensor.shape) == 2:
          num_features = image_shape[0] * image_shape[1] * num_channels
          if int(input_tensor.shape[1]) != num_features:
              raise ValueError(
                  "Image shape and number of channels incompatible with "
                  "input tensor."
              )
      elif len(input_tensor.shape) == 4:
          if (
              int(input_tensor.shape[1]) != image_shape[0]
              or int(input_tensor.shape[2]) != image_shape[1]
              or int(input_tensor.shape[3]) != num_channels
          ):
              raise ValueError(
                  "Image shape and number of channels incompatible with "
                  "input tensor."
              )
      else:
          raise ValueError("Unrecognized input tensor format.")
      height, width = (
          grid_shape[0] * image_shape[0],
          grid_shape[1] * image_shape[1],
      )
      input_tensor = tf.reshape(
          input_tensor, grid_shape + image_shape + [num_channels]
      )
      input_tensor = tf.transpose(input_tensor, [0, 1, 3, 2, 4])
      input_tensor = tf.reshape(
          input_tensor,
          [grid_shape[0], width, image_shape[0], num_channels],
      )
      input_tensor = tf.transpose(input_tensor, [0, 2, 1, 3])
      input_tensor = tf.reshape(
          input_tensor, [1, height, width, num_channels]
      )
      return input_tensor
  
imports: import io
  import os
  import tempfile
  from magenta.models.image_stylization import imagenet_data
  import numpy
  import skimage.io
  import tensorflow.compat.v1
label_desc: Use image node_id 9 for calling a global function with example usage: form_image_grid(input_tensor, grid_shape, image_shape, num_channels) and returns: input_tensor