neulab/docprompting-conala huggingface dataset:
tf.raw_ops.QuantizedMatMulWithBiasAndReluAndRequantize Perform a quantized matrix multiplication of a by the matrix b with bias  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedMatMulWithBiasAndReluAndRequantize  
tf.raw_ops.QuantizedMatMulWithBiasAndReluAndRequantize(
    a, b, bias, min_a, max_a, min_b, max_b, min_freezed_output, max_freezed_output,
    Toutput=tf.dtypes.quint8, transpose_a=False, transpose_b=False,
    input_quant_mode='MIN_FIRST', name=None
)
 add and relu and requantize fusion. The inputs must be two-dimensional matrices and 1D bias vector. And the inner dimension of a (after being transposed if transpose_a is non-zero) must match the outer dimension of b (after being transposed if transposed_b is non-zero). Then do broadcast add operation with bias values on the matrix multiplication result. The bias size must match inner dimension of b. Then do relu activation to get non-negative result. Then do requantize operation to get final uint8 result. Args: a: A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. A matrix to be multiplied. Must be a two-dimensional tensor of type quint8. b: A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. A matrix to be multiplied and must be a two-dimensional tensor of type qint8. bias: A Tensor. Must be one of the following types: float32, qint32. A 1D bias tensor with size matching with inner dimension of b (after being transposed if transposed_b is non-zero). min_a: A Tensor of type float32. The float value that the lowest quantized a value represents. max_a: A Tensor of type float32. The float value that the highest quantized a value represents. min_b: A Tensor of type float32. The float value that the lowest quantized b value represents. max_b: A Tensor of type float32. The float value that the highest quantized b value represents. min_freezed_output: A Tensor of type float32. The float value that the highest quantized output value after requantize. max_freezed_output: A Tensor of type float32. Toutput: An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8. transpose_a: An optional bool. Defaults to False. If true, a is transposed before multiplication. transpose_b: An optional bool. Defaults to False. If true, b is transposed before multiplication. input_quant_mode: An optional string from: "MIN_FIRST", "SCALED". Defaults to "MIN_FIRST". Input data quantization mode. Either MIN_FIRST(default) or SCALED. name: A name for the operation (optional). Returns: A tuple of Tensor objects (out, min_out, max_out). out: A `Tensor` of type `Toutput`.
min_out: A `Tensor` of type `float32`.
max_out: A `Tensor` of type `float32`.
tf.raw_ops.QuantizedMatMulWithBiasAndRequantize  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedMatMulWithBiasAndRequantize  
tf.raw_ops.QuantizedMatMulWithBiasAndRequantize(
    a, b, bias, min_a, max_a, min_b, max_b, min_freezed_output, max_freezed_output,
    Toutput=tf.dtypes.quint8, transpose_a=False, transpose_b=False,
    input_quant_mode='MIN_FIRST', name=None
)

 


 Args
  a   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  b   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  bias   A Tensor. Must be one of the following types: float32, qint32.  
  min_a   A Tensor of type float32.  
  max_a   A Tensor of type float32.  
  min_b   A Tensor of type float32.  
  max_b   A Tensor of type float32.  
  min_freezed_output   A Tensor of type float32.  
  max_freezed_output   A Tensor of type float32.  
  Toutput   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8.  
  transpose_a   An optional bool. Defaults to False.  
  transpose_b   An optional bool. Defaults to False.  
  input_quant_mode   An optional string from: "MIN_FIRST", "SCALED". Defaults to "MIN_FIRST".  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (out, min_out, max_out).     out   A Tensor of type Toutput.  
  min_out   A Tensor of type float32.  
  max_out   A Tensor of type float32.
tf.raw_ops.QuantizedMaxPool Produces the max pool of the input tensor for quantized types.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedMaxPool  
tf.raw_ops.QuantizedMaxPool(
    input, min_input, max_input, ksize, strides, padding, name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The 4D (batch x rows x cols x depth) Tensor to MaxReduce over.  
  min_input   A Tensor of type float32. The float value that the lowest quantized input value represents.  
  max_input   A Tensor of type float32. The float value that the highest quantized input value represents.  
  ksize   A list of ints. The size of the window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input.  
  strides   A list of ints. The stride of the sliding window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, min_output, max_output).     output   A Tensor. Has the same type as input.  
  min_output   A Tensor of type float32.  
  max_output   A Tensor of type float32.
tf.raw_ops.QuantizedMul Returns x * y element-wise, working on quantized buffers.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedMul  
tf.raw_ops.QuantizedMul(
    x, y, min_x, max_x, min_y, max_y, Toutput=tf.dtypes.qint32, name=None
)

 


 Args
  x   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  y   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  min_x   A Tensor of type float32. The float value that the lowest quantized x value represents.  
  max_x   A Tensor of type float32. The float value that the highest quantized x value represents.  
  min_y   A Tensor of type float32. The float value that the lowest quantized y value represents.  
  max_y   A Tensor of type float32. The float value that the highest quantized y value represents.  
  Toutput   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.qint32.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (z, min_z, max_z).     z   A Tensor of type Toutput.  
  min_z   A Tensor of type float32.  
  max_z   A Tensor of type float32.
tf.raw_ops.QuantizeDownAndShrinkRange Convert the quantized 'input' tensor into a lower-precision 'output', using the  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizeDownAndShrinkRange  
tf.raw_ops.QuantizeDownAndShrinkRange(
    input, input_min, input_max, out_type, name=None
)
 actual distribution of the values to maximize the usage of the lower bit depth and adjusting the output min and max ranges accordingly. [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. This operator tries to squeeze as much precision as possible into an output with a lower bit depth by calculating the actual min and max values found in the data. For example, maybe that quint16 input has no values lower than 16,384 and none higher than 49,152. That means only half the range is actually needed, all the float interpretations are between -0.5f and 0.5f, so if we want to compress the data into a quint8 output, we can use that range rather than the theoretical -1.0f to 1.0f that is suggested by the input min and max. In practice, this is most useful for taking output from operations like QuantizedMatMul that can produce higher bit-depth outputs than their inputs and may have large potential output ranges, but in practice have a distribution of input values that only uses a small fraction of the possible range. By feeding that output into this operator, we can reduce it from 32 bits down to 8 with minimal loss of accuracy.
 


 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.  
  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.QuantizedRelu Computes Quantized Rectified Linear: max(features, 0)  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedRelu  
tf.raw_ops.QuantizedRelu(
    features, min_features, max_features, out_type=tf.dtypes.quint8, name=None
)

 


 Args
  features   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  min_features   A Tensor of type float32. The float value that the lowest quantized value represents.  
  max_features   A Tensor of type float32. The float value that the highest quantized value represents.  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (activations, min_activations, max_activations).     activations   A Tensor of type out_type.  
  min_activations   A Tensor of type float32.  
  max_activations   A Tensor of type float32.
tf.raw_ops.QuantizedRelu6 Computes Quantized 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.QuantizedRelu6  
tf.raw_ops.QuantizedRelu6(
    features, min_features, max_features, out_type=tf.dtypes.quint8, name=None
)

 


 Args
  features   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  min_features   A Tensor of type float32. The float value that the lowest quantized value represents.  
  max_features   A Tensor of type float32. The float value that the highest quantized value represents.  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (activations, min_activations, max_activations).     activations   A Tensor of type out_type.  
  min_activations   A Tensor of type float32.  
  max_activations   A Tensor of type float32.
tf.raw_ops.QuantizedReluX Computes Quantized Rectified Linear X: min(max(features, 0), max_value)  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedReluX  
tf.raw_ops.QuantizedReluX(
    features, max_value, min_features, max_features, out_type=tf.dtypes.quint8,
    name=None
)

 


 Args
  features   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  max_value   A Tensor of type float32.  
  min_features   A Tensor of type float32. The float value that the lowest quantized value represents.  
  max_features   A Tensor of type float32. The float value that the highest quantized value represents.  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (activations, min_activations, max_activations).     activations   A Tensor of type out_type.  
  min_activations   A Tensor of type float32.  
  max_activations   A Tensor of type float32.
tf.raw_ops.QuantizedReshape Reshapes a quantized tensor as per the Reshape op.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedReshape  
tf.raw_ops.QuantizedReshape(
    tensor, shape, input_min, input_max, name=None
)
 
<!-- Tabular view -->
 <table class="responsive fixed orange">
<colgroup><col width="214px"><col></colgroup>
<tr><th colspan="2"><h2 class="add-link">Args</h2></th></tr>

<tr>
<td>
`tensor`
</td>
<td>
A `Tensor`.
</td>
</tr><tr>
<td>
`shape`
</td>
<td>
A `Tensor`. Must be one of the following types: `int32`, `int64`.
Defines the shape of the output tensor.
</td>
</tr><tr>
<td>
`input_min`
</td>
<td>
A `Tensor` of type `float32`. The minimum value of the input.
</td>
</tr><tr>
<td>
`input_max`
</td>
<td>
A `Tensor` of type `float32`. The maximum value of the input.
</td>
</tr><tr>
<td>
`name`
</td>
<td>
A name for the operation (optional).
</td>
</tr>
</table>



<!-- Tabular view -->
 <table class="responsive fixed orange">
<colgroup><col width="214px"><col></colgroup>
<tr><th colspan="2"><h2 class="add-link">Returns</h2></th></tr>
<tr class="alt">
<td colspan="2">
A tuple of `Tensor` objects (output, output_min, output_max).
</td>
</tr>
<tr>
<td>
`output`
</td>
<td>
A `Tensor`. Has the same type as `tensor`.
</td>
</tr><tr>
<td>
`output_min`
</td>
<td>
A `Tensor` of type `float32`.
</td>
</tr><tr>
<td>
`output_max`
</td>
<td>
A `Tensor` of type `float32`.
</td>
</tr>
</table>
tf.raw_ops.QuantizedResizeBilinear Resize quantized images to size using quantized bilinear interpolation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedResizeBilinear  
tf.raw_ops.QuantizedResizeBilinear(
    images, size, min, max, align_corners=False, half_pixel_centers=False, name=None
)
 Input images and output images must be quantized types.
 


 Args
  images   A Tensor. Must be one of the following types: quint8, qint32, float32. 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.  
  min   A Tensor of type float32.  
  max   A Tensor of type float32.  
  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 tuple of Tensor objects (resized_images, out_min, out_max).     resized_images   A Tensor. Has the same type as images.  
  out_min   A Tensor of type float32.  
  out_max   A Tensor of type float32.
tf.raw_ops.QueueClose Closes the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueClose  
tf.raw_ops.QueueClose(
    handle, cancel_pending_enqueues=False, name=None
)
 This operation signals that no more elements will be enqueued in the given queue. Subsequent Enqueue(Many) operations will fail. Subsequent Dequeue(Many) operations will continue to succeed if sufficient elements remain in the queue. Subsequent Dequeue(Many) operations that would block will fail immediately.
 


 Args
  handle   A Tensor of type mutable string. The handle to a queue.  
  cancel_pending_enqueues   An optional bool. Defaults to False. If true, all pending enqueue requests that are blocked on the given queue will be canceled.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.QueueCloseV2 Closes the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueCloseV2  
tf.raw_ops.QueueCloseV2(
    handle, cancel_pending_enqueues=False, name=None
)
 This operation signals that no more elements will be enqueued in the given queue. Subsequent Enqueue(Many) operations will fail. Subsequent Dequeue(Many) operations will continue to succeed if sufficient elements remain in the queue. Subsequent Dequeue(Many) operations that would block will fail immediately.
 


 Args
  handle   A Tensor of type resource. The handle to a queue.  
  cancel_pending_enqueues   An optional bool. Defaults to False. If true, all pending enqueue requests that are blocked on the given queue will be canceled.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.QueueDequeue Dequeues a tuple of one or more tensors from the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueDequeue  
tf.raw_ops.QueueDequeue(
    handle, component_types, timeout_ms=-1, name=None
)
 This operation has k outputs, where k is the number of components in the tuples stored in the given queue, and output i is the ith component of the dequeued tuple. N.B. If the queue is empty, this operation will block until an element has been dequeued (or 'timeout_ms' elapses, if specified).
 


 Args
  handle   A Tensor of type mutable string. The handle to a queue.  
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a tuple.  
  timeout_ms   An optional int. Defaults to -1. If the queue is empty, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type component_types.
tf.raw_ops.QueueDequeueMany Dequeues n tuples of one or more tensors from the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueDequeueMany  
tf.raw_ops.QueueDequeueMany(
    handle, n, component_types, timeout_ms=-1, name=None
)
 If the queue is closed and there are fewer than n elements, then an OutOfRange error is returned. This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. All of the components in the dequeued tuple will have size n in the 0th dimension. This operation has k outputs, where k is the number of components in the tuples stored in the given queue, and output i is the ith component of the dequeued tuple. N.B. If the queue is empty, this operation will block until n elements have been dequeued (or 'timeout_ms' elapses, if specified).
 


 Args
  handle   A Tensor of type mutable string. The handle to a queue.  
  n   A Tensor of type int32. The number of tuples to dequeue.  
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a tuple.  
  timeout_ms   An optional int. Defaults to -1. If the queue has fewer than n elements, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type component_types.
tf.raw_ops.QueueDequeueManyV2 Dequeues n tuples of one or more tensors from the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueDequeueManyV2  
tf.raw_ops.QueueDequeueManyV2(
    handle, n, component_types, timeout_ms=-1, name=None
)
 If the queue is closed and there are fewer than n elements, then an OutOfRange error is returned. This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. All of the components in the dequeued tuple will have size n in the 0th dimension. This operation has k outputs, where k is the number of components in the tuples stored in the given queue, and output i is the ith component of the dequeued tuple. N.B. If the queue is empty, this operation will block until n elements have been dequeued (or 'timeout_ms' elapses, if specified).
 


 Args
  handle   A Tensor of type resource. The handle to a queue.  
  n   A Tensor of type int32. The number of tuples to dequeue.  
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a tuple.  
  timeout_ms   An optional int. Defaults to -1. If the queue has fewer than n elements, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type component_types.
tf.raw_ops.QueueDequeueUpTo Dequeues n tuples of one or more tensors from the given queue.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QueueDequeueUpTo  
tf.raw_ops.QueueDequeueUpTo(
    handle, n, component_types, timeout_ms=-1, name=None
)
 This operation is not supported by all queues. If a queue does not support DequeueUpTo, then an Unimplemented error is returned. If the queue is closed and there are more than 0 but less than n elements remaining, then instead of returning an OutOfRange error like QueueDequeueMany, less than n elements are returned immediately. If the queue is closed and there are 0 elements left in the queue, then an OutOfRange error is returned just like in QueueDequeueMany. Otherwise the behavior is identical to QueueDequeueMany: This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. All of the components in the dequeued tuple will have size n in the 0th dimension. This operation has k outputs, where k is the number of components in the tuples stored in the given queue, and output i is the ith component of the dequeued tuple.
 


 Args
  handle   A Tensor of type mutable string. The handle to a queue.  
  n   A Tensor of type int32. The number of tuples to dequeue.  
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a tuple.  
  timeout_ms   An optional int. Defaults to -1. If the queue has fewer than n elements, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet.  
  name   A name for the operation (optional).   
 


 Returns   A list of Tensor objects of type component_types.