code: def transform_take(a, indices, axis):
      r"""Transform ``arr[:,:,:,indices,...]``-like operations into single-dimensional, vector index operations.
  
      This effectively converts certain `AdvancedSubtensor` `Op`\s into a
      combination of `AdvancedSubtensor1`, `Dimshuffle`, and `Reshape` `Op`\s,
      which can be more efficient.
  
      Parameters
      ----------
      a : TensorVariable
          The source array.
      indices : TensorVariable, ndarray, list, tuple
          The indices of the values to extract.
      axis : int
          The axis over which to select values. By default, the flattened
          input array is used.
  
      """
      a = pytensor.tensor.as_tensor_variable(a)
      indices = pytensor.tensor.as_tensor_variable(indices)
      # We can use the more efficient `AdvancedSubtensor1` if `indices` is a vector
      if indices.ndim == 1:
          if axis == 0:
              return advanced_subtensor1(a, indices)
          else:
              shuffle = list(range(a.ndim))
              shuffle[0] = axis
              shuffle[axis] = 0
              res = advanced_subtensor1(
                  a.dimshuffle(shuffle), indices
              ).dimshuffle(shuffle)
              return res
  
      # We can reshape and flatten the indices in order to use an
      # `AdvancedSubtensor1` `Op` per the above
      indices_shape = shape_tuple(indices)
      a_shape = shape_tuple(a)
  
      shape_parts = [
          a_shape[:axis],
          indices_shape,
          a_shape[axis + 1 :],
      ]
  
      shape_parts = [sp for sp in shape_parts if len(sp) > 0]
  
      assert len(shape_parts) > 0
  
      if len(shape_parts) > 1:
          shape = pytensor.tensor.concatenate(shape_parts)
      else:
          shape = shape_parts[0]
  
      ndim = a.ndim + indices.ndim - 1
  
      return transform_take(a, indices.flatten(), axis).reshape(
          shape, ndim=ndim
      )
  
imports: import sys
  from collections.abc import Iterable
  import numpy
  import pytensor
  import pytensor.scalar.basic
  from pytensor import compile
  from pytensor.graph.basic import Constant, Variable
  from pytensor.graph.rewriting.basic import WalkingGraphRewriter, copy_stack_trace, in2out, node_rewriter
  from pytensor.raise_op import Assert
  from pytensor.tensor.basic import Alloc, Join, MakeVector, ScalarFromTensor, TensorFromScalar, alloc, as_tensor, cast, concatenate, extract_constant, get_underlying_scalar_constant_value, register_infer_shape, switch
  from pytensor.tensor.blockwise import Blockwise
  from pytensor.tensor.elemwise import Elemwise
  from pytensor.tensor.exceptions import NotScalarConstantError
  from pytensor.tensor.math import Dot, add, and_, ceil_intdiv, dot, eq, ge, gt, le, lt, maximum, minimum, or_
  from pytensor.tensor.math import all
  from pytensor.tensor.rewriting.basic import register_canonicalize, register_specialize, register_stabilize
  from pytensor.tensor.shape import Shape, SpecifyShape, Unbroadcast, shape_padleft, shape_tuple, specify_shape, unbroadcast
  from pytensor.tensor.sharedvar import TensorSharedVariable
  from pytensor.tensor.subtensor import AdvancedIncSubtensor, AdvancedIncSubtensor1, AdvancedSubtensor, AdvancedSubtensor1, IncSubtensor, Subtensor, advanced_inc_subtensor1, advanced_subtensor, advanced_subtensor1, as_index_constant, as_index_literal, get_canonical_form_slice, get_constant_idx, get_idx_list, get_slice_elements, inc_subtensor, indices_from_subtensor
  from pytensor.tensor.type import TensorType
  from pytensor.tensor.type_other import NoneTypeT, SliceConstant, SliceType
  from pytensor.tensor.variable import TensorConstant, TensorVariable
label_desc: Use image node_id 2 for calling a global function with example usage: transform_take(a, indices, axis) and returns: transform_take, advanced_subtensor1, res