neulab/docprompting-conala huggingface dataset:
tf.raw_ops.Relu6 Computes rectified linear 6: min(max(features, 0), 6).  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Relu6  
tf.raw_ops.Relu6(
    features, name=None
)

 


 Args
  features   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as features.
tf.raw_ops.Relu6Grad Computes rectified linear 6 gradients for a Relu6 operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Relu6Grad  
tf.raw_ops.Relu6Grad(
    gradients, features, name=None
)

 


 Args
  gradients   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The backpropagated gradients to the corresponding Relu6 operation.  
  features   A Tensor. Must have the same type as gradients. The features passed as input to the corresponding Relu6 operation, or its output; using either one produces the same result.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as gradients.
tf.raw_ops.ReluGrad Computes rectified linear gradients for a Relu operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ReluGrad  
tf.raw_ops.ReluGrad(
    gradients, features, name=None
)

 


 Args
  gradients   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The backpropagated gradients to the corresponding Relu operation.  
  features   A Tensor. Must have the same type as gradients. The features passed as input to the corresponding Relu operation, OR the outputs of that operation (both work equivalently).  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as gradients.
tf.raw_ops.RemoteCall Runs function f on a remote device indicated by target.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RemoteCall  
tf.raw_ops.RemoteCall(
    target, args, Tout, f, name=None
)

 


 Args
  target   A Tensor of type string. A fully specified device name where we want to run the function.  
  args   A list of Tensor objects. A list of arguments for the function.  
  Tout   A list of tf.DTypes that has length >= 1. The type list for the return values.  
  f   A function decorated with @Defun. The function to run remotely.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type Tout.
tf.raw_ops.RepeatDataset Creates a dataset that emits the outputs of input_dataset count times.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RepeatDataset  
tf.raw_ops.RepeatDataset(
    input_dataset, count, output_types, output_shapes, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  count   A Tensor of type int64. A scalar representing the number of times that input_dataset should be repeated. A value of -1 indicates that it should be repeated infinitely.  
  output_types   A list of tf.DTypes that has length >= 1.  
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.RequantizationRange Computes a range that covers the actual values present in a quantized tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RequantizationRange  
tf.raw_ops.RequantizationRange(
    input, input_min, input_max, name=None
)
 Given a quantized tensor described by (input, input_min, input_max), outputs a range that covers the actual values present in that tensor. This op is typically used to produce the requested_output_min and requested_output_max for Requantize.
 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  input_min   A Tensor of type float32. The float value that the minimum quantized input value represents.  
  input_max   A Tensor of type float32. The float value that the maximum quantized input value represents.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_min, output_max).     output_min   A Tensor of type float32.  
  output_max   A Tensor of type float32.
tf.raw_ops.RequantizationRangePerChannel Computes requantization range per channel.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RequantizationRangePerChannel  
tf.raw_ops.RequantizationRangePerChannel(
    input, input_min, input_max, clip_value_max, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original input tensor.  
  input_min   A Tensor of type float32. The minimum value of the input tensor  
  input_max   A Tensor of type float32. The maximum value of the input tensor.  
  clip_value_max   A float. The maximum value of the output that needs to be clipped. Example: set this to 6 for Relu6.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output_min, output_max).     output_min   A Tensor of type float32.  
  output_max   A Tensor of type float32.
tf.raw_ops.Requantize Converts the quantized input tensor into a lower-precision output.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Requantize  
tf.raw_ops.Requantize(
    input, input_min, input_max, requested_output_min, requested_output_max,
    out_type, name=None
)
 Converts the quantized input tensor into a lower-precision output, using the output range specified with requested_output_min and requested_output_max. [input_min, input_max] are scalar floats that specify the range for the float interpretation of the input data. For example, if input_min is -1.0f and input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0 value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.
 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  input_min   A Tensor of type float32. The float value that the minimum quantized input value represents.  
  input_max   A Tensor of type float32. The float value that the maximum quantized input value represents.  
  requested_output_min   A Tensor of type float32. The float value that the minimum quantized output value represents.  
  requested_output_max   A Tensor of type float32. The float value that the maximum quantized output value represents.  
  out_type   A tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. The type of the output. Should be a lower bit depth than Tinput.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, output_min, output_max).     output   A Tensor of type out_type.  
  output_min   A Tensor of type float32.  
  output_max   A Tensor of type float32.
tf.raw_ops.RequantizePerChannel Requantizes input with min and max values known per channel.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.RequantizePerChannel  
tf.raw_ops.RequantizePerChannel(
    input, input_min, input_max, requested_output_min, requested_output_max,
    out_type=tf.dtypes.quint8, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original input tensor.  
  input_min   A Tensor of type float32. The minimum value of the input tensor  
  input_max   A Tensor of type float32. The maximum value of the input tensor.  
  requested_output_min   A Tensor of type float32. The minimum value of the output tensor requested.  
  requested_output_max   A Tensor of type float32. The maximum value of the output tensor requested.  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8. The quantized type of output tensor that needs to be converted.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, output_min, output_max).     output   A Tensor of type out_type.  
  output_min   A Tensor of type float32.  
  output_max   A Tensor of type float32.
tf.raw_ops.Reshape Reshapes a tensor.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Reshape  
tf.raw_ops.Reshape(
    tensor, shape, name=None
)
 Given tensor, this operation returns a tensor that has the same values as tensor with shape shape. If one component of 1-D tensor shape is the special value -1, the size of that dimension is computed so that the total size remains constant. In particular, a shape of [-1] flattens into 1-D. At most one component of shape may be unknown. The shape must be 1-D and the operation returns a tensor with shape shape filled with the values of tensor. In this case, the number of elements implied by shape must be the same as the number of elements in tensor. It is an error if shape is not 1-D. For example: # tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
# tensor 't' has shape [9]
reshape(t, [3, 3]) ==> [[1, 2, 3],
                        [4, 5, 6],
                        [7, 8, 9]]

# tensor 't' is [[[1, 1], [2, 2]],
#                [[3, 3], [4, 4]]]
# tensor 't' has shape [2, 2, 2]
reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
                        [3, 3, 4, 4]]

# tensor 't' is [[[1, 1, 1],
#                 [2, 2, 2]],
#                [[3, 3, 3],
#                 [4, 4, 4]],
#                [[5, 5, 5],
#                 [6, 6, 6]]]
# tensor 't' has shape [3, 2, 3]
# pass '[-1]' to flatten 't'
reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]

# -1 can also be used to infer the shape

# -1 is inferred to be 9:
reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]
# -1 is inferred to be 2:
reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]
# -1 is inferred to be 3:
reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
                              [2, 2, 2],
                              [3, 3, 3]],
                             [[4, 4, 4],
                              [5, 5, 5],
                              [6, 6, 6]]]

# tensor 't' is [7]
# shape `[]` reshapes to a scalar
reshape(t, []) ==> 7

 


 Args
  tensor   A Tensor.  
  shape   A Tensor. Must be one of the following types: int32, int64. Defines the shape of the output tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as tensor.
tf.raw_ops.ResizeArea Resize images to size using area interpolation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeArea  
tf.raw_ops.ResizeArea(
    images, size, align_corners=False, name=None
)
 Input images can be of different types but output images are always float. The range of pixel values for the output image might be slightly different from the range for the input image because of limited numerical precision. To guarantee an output range, for example [0.0, 1.0], apply tf.clip_by_value to the output. Each output pixel is computed by first transforming the pixel's footprint into the input tensor and then averaging the pixels that intersect the footprint. An input pixel's contribution to the average is weighted by the fraction of its area that intersects the footprint. This is the same as OpenCV's INTER_AREA.
 


 Args
  images   A Tensor. Must be one of the following types: int8, uint8, int16, uint16, int32, int64, half, float32, float64, bfloat16. 4-D with shape [batch, height, width, channels].  
  size   A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.  
  align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Defaults to false.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.ResizeBicubic Resize images to size using bicubic interpolation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeBicubic  
tf.raw_ops.ResizeBicubic(
    images, size, align_corners=False, half_pixel_centers=False, name=None
)
 Input images can be of different types but output images are always float.
 


 Args
  images   A Tensor. Must be one of the following types: int8, uint8, int16, uint16, int32, int64, half, float32, float64, bfloat16. 4-D with shape [batch, height, width, channels].  
  size   A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.  
  align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Defaults to false.  
  half_pixel_centers   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.ResizeBicubicGrad Computes the gradient of bicubic interpolation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeBicubicGrad  
tf.raw_ops.ResizeBicubicGrad(
    grads, original_image, align_corners=False, half_pixel_centers=False, name=None
)

 


 Args
  grads   A Tensor of type float32. 4-D with shape [batch, height, width, channels].  
  original_image   A Tensor. Must be one of the following types: float32, float64. 4-D with shape [batch, orig_height, orig_width, channels], The image tensor that was resized.  
  align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and grad tensors are aligned. Defaults to false.  
  half_pixel_centers   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as original_image.
tf.raw_ops.ResizeBilinear Resize images to size using bilinear interpolation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeBilinear  
tf.raw_ops.ResizeBilinear(
    images, size, align_corners=False, half_pixel_centers=False, name=None
)
 Input images can be of different types but output images are always float.
 


 Args
  images   A Tensor. Must be one of the following types: int8, uint8, int16, uint16, int32, int64, bfloat16, half, float32, float64, bfloat16. 4-D with shape [batch, height, width, channels].  
  size   A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.  
  align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Defaults to false.  
  half_pixel_centers   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.ResizeBilinearGrad Computes the gradient of bilinear interpolation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeBilinearGrad  
tf.raw_ops.ResizeBilinearGrad(
    grads, original_image, align_corners=False, half_pixel_centers=False, name=None
)

 


 Args
  grads   A Tensor of type float32. 4-D with shape [batch, height, width, channels].  
  original_image   A Tensor. Must be one of the following types: float32, bfloat16, half, float64. 4-D with shape [batch, orig_height, orig_width, channels], The image tensor that was resized.  
  align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and grad tensors are aligned. Defaults to false.  
  half_pixel_centers   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as original_image.
tf.raw_ops.ResizeNearestNeighbor Resize images to size using nearest neighbor interpolation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeNearestNeighbor  
tf.raw_ops.ResizeNearestNeighbor(
    images, size, align_corners=False, half_pixel_centers=False, name=None
)

 


 Args
  images   A Tensor. Must be one of the following types: int8, uint8, int16, uint16, int32, int64, half, float32, float64, bfloat16. 4-D with shape [batch, height, width, channels].  
  size   A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.  
  align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Defaults to false.  
  half_pixel_centers   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as images.
tf.raw_ops.ResizeNearestNeighborGrad Computes the gradient of nearest neighbor interpolation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResizeNearestNeighborGrad  
tf.raw_ops.ResizeNearestNeighborGrad(
    grads, size, align_corners=False, half_pixel_centers=False, name=None
)

 


 Args
  grads   A Tensor. Must be one of the following types: uint8, int8, int32, half, float32, float64. 4-D with shape [batch, height, width, channels].  
  size   A 1-D int32 Tensor of 2 elements: orig_height, orig_width. The original input size.  
  align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and grad tensors are aligned. Defaults to false.  
  half_pixel_centers   An optional bool. Defaults to False.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as grads.
tf.raw_ops.ResourceAccumulatorApplyGradient Applies a gradient to a given accumulator.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceAccumulatorApplyGradient  
tf.raw_ops.ResourceAccumulatorApplyGradient(
    handle, local_step, gradient, name=None
)
 Does not add if local_step is lesser than the accumulator's global_step.
 


 Args
  handle   A Tensor of type resource. The handle to a accumulator.  
  local_step   A Tensor of type int64. The local_step value at which the gradient was computed.  
  gradient   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A tensor of the gradient to be accumulated.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceAccumulatorNumAccumulated Returns the number of gradients aggregated in the given accumulators.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceAccumulatorNumAccumulated  
tf.raw_ops.ResourceAccumulatorNumAccumulated(
    handle, name=None
)

 


 Args
  handle   A Tensor of type resource. The handle to an accumulator.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type int32.
tf.raw_ops.ResourceAccumulatorSetGlobalStep Updates the accumulator with a new value for global_step.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceAccumulatorSetGlobalStep  
tf.raw_ops.ResourceAccumulatorSetGlobalStep(
    handle, new_global_step, name=None
)
 Logs warning if the accumulator's value is already higher than new_global_step.
 


 Args
  handle   A Tensor of type resource. The handle to an accumulator.  
  new_global_step   A Tensor of type int64. The new global_step value to set.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceAccumulatorTakeGradient Extracts the average gradient in the given ConditionalAccumulator.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceAccumulatorTakeGradient  
tf.raw_ops.ResourceAccumulatorTakeGradient(
    handle, num_required, dtype, name=None
)
 The op blocks until sufficient (i.e., more than num_required) gradients have been accumulated. If the accumulator has already aggregated more than num_required gradients, it returns the average of the accumulated gradients. Also automatically increments the recorded global_step in the accumulator by 1, and resets the aggregate to 0.
 


 Args
  handle   A Tensor of type resource. The handle to an accumulator.  
  num_required   A Tensor of type int32. Number of gradients required before we return an aggregate.  
  dtype   A tf.DType from: tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64, tf.qint8, tf.quint8, tf.qint32, tf.bfloat16, tf.uint16, tf.complex128, tf.half, tf.uint32, tf.uint64. The data type of accumulated gradients. Needs to correspond to the type of the accumulator.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type dtype.
tf.raw_ops.ResourceApplyAdadelta Update '*var' according to the adadelta scheme.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdadelta  
tf.raw_ops.ResourceApplyAdadelta(
    var, accum, accum_update, lr, rho, epsilon, grad, use_locking=False, name=None
)
 accum = rho() * accum + (1 - rho()) * grad.square(); update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; update_accum = rho() * update_accum + (1 - rho()) * update.square(); var -= update;
 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  accum   A Tensor of type resource. Should be from a Variable().  
  accum_update   A Tensor of type resource. Should be from a Variable().  
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.  
  rho   A Tensor. Must have the same type as lr. Decay factor. Must be a scalar.  
  epsilon   A Tensor. Must have the same type as lr. Constant factor. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. The gradient.  
  use_locking   An optional bool. Defaults to False. If True, updating of the var, accum and update_accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.ResourceApplyAdagrad Update '*var' according to the adagrad scheme.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ResourceApplyAdagrad  
tf.raw_ops.ResourceApplyAdagrad(
    var, accum, lr, grad, use_locking=False, update_slots=True, name=None
)
 accum += grad * grad var -= lr * grad * (1 / sqrt(accum))
 


 Args
  var   A Tensor of type resource. Should be from a Variable().  
  accum   A Tensor of type resource. Should be from a Variable().  
  lr   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Scaling factor. Must be a scalar.  
  grad   A Tensor. Must have the same type as lr. The gradient.  
  use_locking   An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  update_slots   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.