code: def concatenate(values, axis=0, like=None):
      """Concatenate 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
              concatenate. The objects must have the same shape, except in the dimension corresponding
              to axis (the first, by default).
          axis (int): The axis along which the input tensors are concatenated. If axis is None,
              tensors are flattened before use. Default is 0.
  
      Returns:
          tensor_like: The concatenated tensor.
  
      **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.])
      >>> concatenate([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)>
      """
  
      if like == "torch":
          import torch
  
          device = (
              "cuda"
              if any(
                  t.device.type == "cuda"
                  for t in values
                  if isinstance(t, torch.Tensor)
              )
              else "cpu"
          )
  
          if axis is None:
              # flatten and then concatenate zero'th dimension
              # to reproduce numpy's behaviour
              values = [
                  np.flatten(
                      torch.as_tensor(t, device=torch.device(device))
                  )  # pragma: no cover
                  for t in values
              ]
              axis = 0
          else:
              values = [
                  torch.as_tensor(t, device=torch.device(device))
                  for t in values  # pragma: no cover
              ]
  
      if like == "tensorflow" and axis is None:
          # flatten and then concatenate zero'th dimension
          # to reproduce numpy's behaviour
          values = [np.flatten(np.array(t)) for t in values]
          axis = 0
  
      return np.concatenate(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 6 for calling a global function with example usage: concatenate(values, axis, like) and returns: np