code: def einsum(indices, *operands, like=None, optimize=None):
      """Evaluates the Einstein summation convention on the operands.
  
      Args:
          indices (str): Specifies the subscripts for summation as comma separated list of
              subscript labels. An implicit (classical Einstein summation) calculation is
              performed unless the explicit indicator ‘->’ is included as well as subscript
              labels of the precise output form.
          *operands (tuple[tensor_like]): The tensors for the operation.
  
      Returns:
          tensor_like: The calculation based on the Einstein summation convention.
  
      **Examples**
  
      >>> a = np.arange(25).reshape(5,5)
      >>> b = np.arange(5)
      >>> c = np.arange(6).reshape(2,3)
  
      Trace of a matrix:
  
      >>> qml.math.einsum('ii', a)
      60
  
      Extract the diagonal (requires explicit form):
  
      >>> qml.math.einsum('ii->i', a)
      array([ 0,  6, 12, 18, 24])
  
      Sum over an axis (requires explicit form):
  
      >>> qml.math.einsum('ij->i', a)
      array([ 10,  35,  60,  85, 110])
  
      Compute a matrix transpose, or reorder any number of axes:
  
      >>> np.einsum('ij->ji', c)
      array([[0, 3],
             [1, 4],
             [2, 5]])
  
      Matrix vector multiplication:
  
      >>> np.einsum('ij,j', a, b)
      array([ 30,  80, 130, 180, 230])
      """
      if like is None:
          like = get_interface(*operands)
      operands = np.coerce(operands, like=like)
      if optimize is None or like == "torch":
          # torch einsum doesn't support the optimize keyword argument
          return np.einsum(indices, *operands, like=like)
      if like == "tensorflow":
          # Unpacking and casting necessary for higher order derivatives,
          # and avoiding implicit fp32 down-conversions.
          op1, op2 = operands
          op1 = array(op1, like=op1[0], dtype=op1[0].dtype)
          op2 = array(op2, like=op2[0], dtype=op2[0].dtype)
          return np.einsum(indices, op1, op2, like=like)
      return np.einsum(indices, *operands, like=like, optimize=optimize)
  
imports: import functools
  from collections.abc import Sequence
  import autoray
  import numpy
  from autograd.numpy.numpy_boxes import ArrayBox
  from autoray import numpy
  from numpy import ndarray
  from .None import single_dispatch
  from .utils import cast, cast_like, get_interface, requires_grad
label_desc: Use image node_id 14 for calling a global function with example usage: einsum(indices) and returns: np, np, np