code: def stack(values, axis=0, like=None):
      """Stack a sequence of tensors along the specified axis.
  
      .. warning::
  
          Tensors that are incompatible (such as Torch and TensorFlow tensors)
          cannot both be present.
  
      Args:
          values (Sequence[tensor_like]): Sequence of tensor-like objects to
              stack. Each object in the sequence must have the same size in the given axis.
          axis (int): The axis along which the input tensors are stacked. ``axis=0`` corresponds
              to vertical stacking.
  
      Returns:
          tensor_like: The stacked array. The stacked array will have one additional dimension
          compared to the unstacked tensors.
  
      **Example**
  
      >>> x = tf.constant([0.6, 0.1, 0.6])
      >>> y = tf.Variable([0.1, 0.2, 0.3])
      >>> z = np.array([5., 8., 101.])
      >>> stack([x, y, z])
      <tf.Tensor: shape=(3, 3), dtype=float32, numpy=
      array([[6.00e-01, 1.00e-01, 6.00e-01],
             [1.00e-01, 2.00e-01, 3.00e-01],
             [5.00e+00, 8.00e+00, 1.01e+02]], dtype=float32)>
      """
      values = np.coerce(values, like=like)
      return np.stack(values, axis=axis, like=like)
  
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 13 for calling a global function with example usage: stack(values, axis, like) and returns: np