code: def get_batch_size(tensor, expected_shape, expected_size):
      """
      Determine whether a tensor has an additional batch dimension for broadcasting,
      compared to an expected_shape. Has support for abstract TF tensors.
  
      Args:
          tensor (TensorLike): A tensor to inspect for batching
          expected_shape (Tuple[int]): The expected shape of the tensor if not batched
          expected_size (int): The expected size of the tensor if not batched
  
      Returns:
          Optional[int]: The batch size of the tensor if there is one, otherwise None
      """
      try:
          size = qml.math.size(tensor)
          ndim = qml.math.ndim(tensor)
          if ndim > len(expected_shape) or size > expected_size:
              return size // expected_size
  
      except (
          Exception
      ) as err:  # pragma: no cover, pylint:disable=broad-except
          # This except clause covers the usage of tf.function
          if not qml.math.is_abstract(tensor):
              raise err
  
      return None
  
file_path: pennylane/pennylane/math/matrix_manipulation.py
imports: import itertools
  from functools import reduce
  from typing import Generator, Iterable, Tuple
  import numpy
  from scipy.sparse import csr_matrix, eye, kron
  import pennylane
  from pennylane.wires import Wires
label_desc: Use image node_id 7 for calling a global function with example usage: get_batch_size(tensor, expected_shape, expected_size) and returns: None, unknown