class_docstr: Concatenate multiple tensors towards specified axis.
class_docstr_tok: ["Concatenate","multiple","tensors","towards","specified","axis","."]
code: def _forward_ideep(self, xs):
      xs_mdarray = intel64.ideep.mdarrayVector()
      for x in xs:
          xs_mdarray.push_back(intel64.ideep.array(x))
      ndim = xs[0].ndim
      axis = self.axis % ndim
      return (intel64.ideep.concat.Forward(xs_mdarray, axis),)
  
file_path: chainer/chainer/functions/array/concat.py
imports: import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import intel64
  from chainer import function_node
  from chainer.utils import type_check
  import chainerx
label_desc: Use image node_id 5 for calling the Concat obj's underlying member method code with example usage: obj._forward_ideep(xs) and returns: intel64