parameters: (device, input_shape, filter_shape, strides,
                                       padding, num_iters, data_format)
docstring: builds a graph containing a sequence of conv2d operations.
  
    Args:
      device: String, the device to run on.
      input_shape: Shape of the input tensor.
      filter_shape: Shape of the filter tensor.
      strides: A list of ints. 1-D of length 4. The stride of sliding
               window for each dimension of input.
      padding: A string from: "SAME", "VALID". The type of padding
               algorithm to use.
      num_iters: number of iterations to run conv2d.
      data_format: data format string of input, 'NHWC' and 'NCHW' are
      supported.
  
    Returns:
      An array of tensors to run()
docstring_summary: builds a graph containing a sequence of conv2d operations.
docstring_tokens: builds a graph containing a sequence of conv2d operations .
function: def build_fused_conv_bias_relu_graph(device, input_shape, filter_shape, strides,
                                       padding, num_iters, data_format):
    """builds a graph containing a sequence of conv2d operations.
  
    Args:
      device: String, the device to run on.
      input_shape: Shape of the input tensor.
      filter_shape: Shape of the filter tensor.
      strides: A list of ints. 1-D of length 4. The stride of sliding
               window for each dimension of input.
      padding: A string from: "SAME", "VALID". The type of padding
               algorithm to use.
      num_iters: number of iterations to run conv2d.
      data_format: data format string of input, 'NHWC' and 'NCHW' are
      supported.
  
    Returns:
      An array of tensors to run()
    """
    if data_format == "NCHW":
      input_shape = [
          input_shape[0], input_shape[3], input_shape[1], input_shape[2]
      ]
    with ops.device("/%s:0" % device):
      inp = variables.Variable(random_ops.truncated_normal(input_shape))
      filt = variables.Variable(random_ops.truncated_normal(filter_shape))
      bias_shape = [filter_shape[-1]]
      bias = variables.Variable(random_ops.truncated_normal(bias_shape))
  
      outputs = []
      fused_out = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
          inp,
          filt,
          bias,
          strides,
          padding,
          data_format=data_format,
          activation_mode="Relu")
      outputs.append(fused_out)
      for _ in range(1, num_iters):
        with ops.control_dependencies([fused_out]):
          # pylint: disable=g-line-too-long
          fused_out = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(  # pylint: disable=line-too-long
              inp,
              filt,
              bias,
              strides,
              padding,
              data_format=data_format,
              activation_mode="Relu")
          outputs.append(fused_out)
      return control_flow_ops.group(*outputs)
function_tokens: def build_fused_conv_bias_relu_graph ( device , input_shape , filter_shape , strides , padding , num_iters , data_format ) : if data_format == "NCHW" : input_shape = [ input_shape [ 0 ] , input_shape [ 3 ] , input_shape [ 1 ] , input_shape [ 2 ] ] with ops . device ( "/%s:0" % device ) : inp = variables . Variable ( random_ops . truncated_normal ( input_shape ) ) filt = variables . Variable ( random_ops . truncated_normal ( filter_shape ) ) bias_shape = [ filter_shape [ - 1 ] ] bias = variables . Variable ( random_ops . truncated_normal ( bias_shape ) ) outputs = [ ] fused_out = fused_conv2d_bias_activation_op . fused_conv2d_bias_activation ( inp , filt , bias , strides , padding , data_format = data_format , activation_mode = "Relu" ) outputs . append ( fused_out ) for _ in range ( 1 , num_iters ) : with ops . control_dependencies ( [ fused_out ] ) : # pylint: disable=g-line-too-long fused_out = fused_conv2d_bias_activation_op . fused_conv2d_bias_activation ( # pylint: disable=line-too-long inp , filt , bias , strides , padding , data_format = data_format , activation_mode = "Relu" ) outputs . append ( fused_out ) return control_flow_ops . group ( * outputs )