code: def match_intermediate_batch_dimensions(tensor1, tensor2):
      """Match the batch dimensions.
  
      Args:
        tensor1: A tensor of shape `[A1, M]`.
        tensor2: A tensor of shape `[A1, ..., An, N]`.
      Returns:
        A tensor of shape `[A1, ..., An, M]`.
      """
      shape.check_static(
          tensor=tensor1, tensor_name="tensor1", has_rank=2
      )
      shape.check_static(
          tensor=tensor2, tensor_name="tensor2", has_rank_greater_than=1
      )
      shape.compare_dimensions(
          tensors=(tensor1, tensor2),
          tensor_names=("tensor1", "tensor2"),
          axes=0,
      )
  
      shape1 = tf.shape(tensor1)
      shape2 = tf.shape(tensor2)
      shape_diff = len(shape2) - len(shape1)
      new_shape = tf.concat(
          [[shape1[0]], [1] * shape_diff, [shape1[-1]]], axis=-1
      )
      target_shape = tf.concat([shape2[:-1], [shape1[-1]]], axis=-1)
      return tf.broadcast_to(
          tf.reshape(tensor1, new_shape), target_shape
      )
  
file_path: graphics/tensorflow_graphics/projects/radiance_fields/utils.py
imports: import math
  import tensorflow
  from tensorflow_graphics.geometry.transformation import rotation_matrix_3d
  from tensorflow_graphics.util import shape
label_desc: Use image node_id 1 for calling a global function with example usage: match_intermediate_batch_dimensions(tensor1, tensor2) and returns: tf