class_docstr: Concatenate multiple tensors towards specified axis.
class_docstr_tok: ["Concatenate","multiple","tensors","towards","specified","axis","."]
code: def forward(self, xs):
      if intel64.should_use_ideep(
          ">=auto"
      ) and intel64.inputs_all_ready(xs, (4,)):
          # iDeep implementation
          return self._forward_ideep(xs)
  
      # Generic implementation
      xp = backend.get_array_module(*xs)
      return (xp.concatenate(xs, self.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 3 for calling the Concat obj's underlying member method code with example usage: obj.forward(xs) and returns: xp, self