code: def smart_array_concatenate(arrays):
      """
      Concatenate the arrays in ``arrays``, smartly handling 1D broadcasted arrays that contain all
      contain an identical value, and when ``arrays`` contains a single array.
      """
      if len(arrays) == 1:
          # concatenate allocates a new array even in this case, so we can avoid a copy
          return arrays[0]
  
      arrays = [np.asanyarray(arr) for arr in arrays]
  
      # check whether all of the arrays contain a single value, broadcasted
      nonempty = (arr for arr in arrays if len(arr) > 0)
      first = next(nonempty, None)
      if first is not None:
          element = first[0]
  
          def check(arr):
              return (
                  len(arr.shape) == 1
                  and arr.strides[0] == 0
                  and arr[0] == element
              )
  
          if check(first) and all(check(arr) for arr in nonempty):
              # concatenate will always allocate a whole new array, which is suboptimal for edge
              # weights, where they may all be broadcasted 1s
              total_len = sum(len(arr) for arr in arrays)
              # rebroadcast the element to the final result shape
              return np.broadcast_to(element, total_len)
  
      # fallback to the default behaviour
      return np.concatenate(arrays)
  
file_path: stellargraph/stellargraph/core/utils.py
imports: import collections
  import scipy.sparse
  from scipy.sparse.linalg import ArpackNoConvergence, eigsh
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: smart_array_concatenate(arrays) and returns: np, arrays, unknown, np