parameters: (values: tuple or list, dim: Shape)
return_statement: return NativeTensor(concatenated, broadcast_shape.with_sizes(backend.staticshape(concatenated)))
docstring: Concatenates a sequence of tensors along one dimension.
      The shapes of all values must be equal, except for the size of the concat dimension.
  
      Args:
          values: Tensors to concatenate
          dim: Concatenation dimension, must be present in all `values`.
              The size along `dim` is determined from `values` and can be set to undefined (`None`).
  
      Returns:
          Concatenated `Tensor`
docstring_summary: Concatenates a sequence of tensors along one dimension.
      The shapes of all values must be equal, except for the size of the concat dimension.
docstring_tokens: Concatenates a sequence of tensors along one dimension . The shapes of all values must be equal except for the size of the concat dimension .
function: def concat(values: tuple or list, dim: Shape) -> Tensor:
      """
      Concatenates a sequence of tensors along one dimension.
      The shapes of all values must be equal, except for the size of the concat dimension.
  
      Args:
          values: Tensors to concatenate
          dim: Concatenation dimension, must be present in all `values`.
              The size along `dim` is determined from `values` and can be set to undefined (`None`).
  
      Returns:
          Concatenated `Tensor`
      """
      assert len(values) > 0, "concat() got empty sequence"
      assert isinstance(dim, Shape) and dim.rank == 1, f"dim must be a single-dimension Shape but got '{dim}' of type {type(dim)}"
      broadcast_shape = values[0].shape
      natives = [v.native(order=broadcast_shape.names) for v in values]
      backend = choose_backend(*natives)
      concatenated = backend.concat(natives, broadcast_shape.index(dim))
      return NativeTensor(concatenated, broadcast_shape.with_sizes(backend.staticshape(concatenated)))
function_tokens: def concat ( values : tuple or list , dim : Shape ) -> Tensor : assert len ( values ) > 0 , "concat() got empty sequence" assert isinstance ( dim , Shape ) and dim . rank == 1 , f"dim must be a single-dimension Shape but got '{dim}' of type {type(dim)}" broadcast_shape = values [ 0 ] . shape natives = [ v . native ( order = broadcast_shape . names ) for v in values ] backend = choose_backend ( * natives ) concatenated = backend . concat ( natives , broadcast_shape . index ( dim ) ) return NativeTensor ( concatenated , broadcast_shape . with_sizes ( backend . staticshape ( concatenated ) ) )