parameters: (input_shape,
                          default_size,
                          min_size,
                          data_format,
                          require_flatten,
                          weights=None)
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.
        require_flatten: whether the model is expected to
            be linked to a classifier via a Flatten layer.
        weights: one of `None` (random initialization)
            or 'imagenet' (pre-training on ImageNet).
            If weights='imagenet' input channels must be equal to 3.
  
    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,
                          require_flatten,
                          weights=None):
    """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.
        require_flatten: whether the model is expected to
            be linked to a classifier via a Flatten layer.
        weights: one of `None` (random initialization)
            or 'imagenet' (pre-training on ImageNet).
            If weights='imagenet' input channels must be equal to 3.
  
    Returns:
        An integer shape tuple (may include None entries).
  
    Raises:
        ValueError: in case of invalid argument values.
    """
    if weights != 'imagenet' and input_shape and len(input_shape) == 3:
      if data_format == 'channels_first':
        if input_shape[0] not in {1, 3}:
          logging.warning('This model usually expects 1 or 3 input channels. '
                          'However, it was passed an input_shape with ' +
                          str(input_shape[0]) + ' input channels.')
        default_shape = (input_shape[0], default_size, default_size)
      else:
        if input_shape[-1] not in {1, 3}:
          logging.warning('This model usually expects 1 or 3 input channels. '
                          'However, it was passed an input_shape with ' +
                          str(input_shape[-1]) + ' input channels.')
        default_shape = (default_size, default_size, input_shape[-1])
    else:
      if data_format == 'channels_first':
        default_shape = (3, default_size, default_size)
      else:
        default_shape = (default_size, default_size, 3)
    if weights == 'imagenet' and require_flatten:
      if input_shape is not None:
        if input_shape != default_shape:
          raise ValueError('When setting`include_top=True` '
                           'and loading `imagenet` weights, '
                           '`input_shape` should be ' + str(default_shape) + '.')
      return default_shape
    if input_shape:
      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 and weights == 'imagenet':
            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:
        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 and weights == 'imagenet':
            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:
      if require_flatten:
        input_shape = default_shape
      else:
        if data_format == 'channels_first':
          input_shape = (3, None, None)
        else:
          input_shape = (None, None, 3)
    if require_flatten:
      if None in input_shape:
        raise ValueError('If `include_top` is True, '
                         'you should specify a static `input_shape`. '
                         'Got `input_shape=' + str(input_shape) + '`')
    return input_shape
function_tokens: def _obtain_input_shape ( input_shape , default_size , min_size , data_format , require_flatten , weights = None ) : if weights != 'imagenet' and input_shape and len ( input_shape ) == 3 : if data_format == 'channels_first' : if input_shape [ 0 ] not in { 1 , 3 } : logging . warning ( 'This model usually expects 1 or 3 input channels. ' 'However, it was passed an input_shape with ' + str ( input_shape [ 0 ] ) + ' input channels.' ) default_shape = ( input_shape [ 0 ] , default_size , default_size ) else : if input_shape [ - 1 ] not in { 1 , 3 } : logging . warning ( 'This model usually expects 1 or 3 input channels. ' 'However, it was passed an input_shape with ' + str ( input_shape [ - 1 ] ) + ' input channels.' ) default_shape = ( default_size , default_size , input_shape [ - 1 ] ) else : if data_format == 'channels_first' : default_shape = ( 3 , default_size , default_size ) else : default_shape = ( default_size , default_size , 3 ) if weights == 'imagenet' and require_flatten : if input_shape is not None : if input_shape != default_shape : raise ValueError ( 'When setting`include_top=True` ' 'and loading `imagenet` weights, ' '`input_shape` should be ' + str ( default_shape ) + '.' ) return default_shape if input_shape : 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 and weights == 'imagenet' : 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 : 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 and weights == 'imagenet' : 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 : if require_flatten : input_shape = default_shape else : if data_format == 'channels_first' : input_shape = ( 3 , None , None ) else : input_shape = ( None , None , 3 ) if require_flatten : if None in input_shape : raise ValueError ( 'If `include_top` is True, ' 'you should specify a static `input_shape`. ' 'Got `input_shape=' + str ( input_shape ) + '`' ) return input_shape