parameters: (input_shape, default_size, min_size, data_format,
                          include_top)
return_statement: return input_shape
docstring: Internal utility to compute/validate an ImageNet model's input shape.
  
    Arguments:
        input_shape: either None (will return the default network input shape),
            or a user-provided shape to be validated.
        default_size: default input width/height for the model.
        min_size: minimum input width/height accepted by the model.
        data_format: image data format to use.
        include_top: whether the model is expected to
            be linked to a classifier via a Flatten layer.
  
    Returns:
        An integer shape tuple (may include None entries).
  
    Raises:
        ValueError: in case of invalid argument values.
docstring_summary: Internal utility to compute/validate an ImageNet model's input shape.
docstring_tokens: Internal utility to compute / validate an ImageNet model s input shape .
function: def _obtain_input_shape(input_shape, default_size, min_size, data_format,
                          include_top):
    """Internal utility to compute/validate an ImageNet model's input shape.
  
    Arguments:
        input_shape: either None (will return the default network input shape),
            or a user-provided shape to be validated.
        default_size: default input width/height for the model.
        min_size: minimum input width/height accepted by the model.
        data_format: image data format to use.
        include_top: whether the model is expected to
            be linked to a classifier via a Flatten layer.
  
    Returns:
        An integer shape tuple (may include None entries).
  
    Raises:
        ValueError: in case of invalid argument values.
    """
    if data_format == 'channels_first':
      default_shape = (3, default_size, default_size)
    else:
      default_shape = (default_size, default_size, 3)
    if include_top:
      if input_shape is not None:
        if input_shape != default_shape:
          raise ValueError('When setting`include_top=True`, '
                           '`input_shape` should be ' + str(default_shape) + '.')
      input_shape = default_shape
    else:
      if data_format == 'channels_first':
        if input_shape is not None:
          if len(input_shape) != 3:
            raise ValueError('`input_shape` must be a tuple of three integers.')
          if input_shape[0] != 3:
            raise ValueError('The input must have 3 channels; got '
                             '`input_shape=' + str(input_shape) + '`')
          if ((input_shape[1] is not None and input_shape[1] < min_size) or
              (input_shape[2] is not None and input_shape[2] < min_size)):
            raise ValueError('Input size must be at least ' + str(min_size) + 'x'
                             + str(min_size) + ', got '
                             '`input_shape=' + str(input_shape) + '`')
        else:
          input_shape = (3, None, None)
      else:
        if input_shape is not None:
          if len(input_shape) != 3:
            raise ValueError('`input_shape` must be a tuple of three integers.')
          if input_shape[-1] != 3:
            raise ValueError('The input must have 3 channels; got '
                             '`input_shape=' + str(input_shape) + '`')
          if ((input_shape[0] is not None and input_shape[0] < min_size) or
              (input_shape[1] is not None and input_shape[1] < min_size)):
            raise ValueError('Input size must be at least ' + str(min_size) + 'x'
                             + str(min_size) + ', got '
                             '`input_shape=' + str(input_shape) + '`')
        else:
          input_shape = (None, None, 3)
    return input_shape
function_tokens: def _obtain_input_shape ( input_shape , default_size , min_size , data_format , include_top ) : if data_format == 'channels_first' : default_shape = ( 3 , default_size , default_size ) else : default_shape = ( default_size , default_size , 3 ) if include_top : if input_shape is not None : if input_shape != default_shape : raise ValueError ( 'When setting`include_top=True`, ' '`input_shape` should be ' + str ( default_shape ) + '.' ) input_shape = default_shape else : if data_format == 'channels_first' : if input_shape is not None : if len ( input_shape ) != 3 : raise ValueError ( '`input_shape` must be a tuple of three integers.' ) if input_shape [ 0 ] != 3 : raise ValueError ( 'The input must have 3 channels; got ' '`input_shape=' + str ( input_shape ) + '`' ) if ( ( input_shape [ 1 ] is not None and input_shape [ 1 ] < min_size ) or ( input_shape [ 2 ] is not None and input_shape [ 2 ] < min_size ) ) : raise ValueError ( 'Input size must be at least ' + str ( min_size ) + 'x' + str ( min_size ) + ', got ' '`input_shape=' + str ( input_shape ) + '`' ) else : input_shape = ( 3 , None , None ) else : if input_shape is not None : if len ( input_shape ) != 3 : raise ValueError ( '`input_shape` must be a tuple of three integers.' ) if input_shape [ - 1 ] != 3 : raise ValueError ( 'The input must have 3 channels; got ' '`input_shape=' + str ( input_shape ) + '`' ) if ( ( input_shape [ 0 ] is not None and input_shape [ 0 ] < min_size ) or ( input_shape [ 1 ] is not None and input_shape [ 1 ] < min_size ) ) : raise ValueError ( 'Input size must be at least ' + str ( min_size ) + 'x' + str ( min_size ) + ', got ' '`input_shape=' + str ( input_shape ) + '`' ) else : input_shape = ( None , None , 3 ) return input_shape