parameters: (images, static_only=True)
docstring: Returns the dimensions of an image tensor.
  
    Args:
      images: 4-D Tensor of shape `[batch, height, width, channels]`
      static_only: Boolean, whether to return only static shape.
  
    Returns:
      list of integers `[batch, height, width, channels]`, when static shape is
      fully defined or `static_only` is `True`.
      list of integer scalar tensors `[batch, height, width, channels]`, when
      static shape is not fully defined.
docstring_summary: Returns the dimensions of an image tensor.
docstring_tokens: Returns the dimensions of an image tensor .
function: def _ImageDimensions(images, static_only=True):
    """Returns the dimensions of an image tensor.
  
    Args:
      images: 4-D Tensor of shape `[batch, height, width, channels]`
      static_only: Boolean, whether to return only static shape.
  
    Returns:
      list of integers `[batch, height, width, channels]`, when static shape is
      fully defined or `static_only` is `True`.
      list of integer scalar tensors `[batch, height, width, channels]`, when
      static shape is not fully defined.
    """
    # A simple abstraction to provide names for each dimension. This abstraction
    # should make it simpler to switch dimensions in the future (e.g. if we ever
    # want to switch height and width.)
    if static_only or images.get_shape().is_fully_defined():
      return images.get_shape().as_list()
    else:
      return array_ops.unpack(array_ops.shape(images))
function_tokens: def _ImageDimensions ( images , static_only = True ) : # A simple abstraction to provide names for each dimension. This abstraction # should make it simpler to switch dimensions in the future (e.g. if we ever # want to switch height and width.) if static_only or images . get_shape ( ) . is_fully_defined ( ) : return images . get_shape ( ) . as_list ( ) else : return array_ops . unpack ( array_ops . shape ( images ) )