neulab/docprompting-conala huggingface dataset:
tf.raw_ops.QuantizedConv2DWithBiasAndRequantize  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DWithBiasAndRequantize  
tf.raw_ops.QuantizedConv2DWithBiasAndRequantize(
    input, filter, bias, min_input, max_input, min_filter, max_filter,
    min_freezed_output, max_freezed_output, strides, padding,
    out_type=tf.dtypes.qint8, dilations=[1, 1, 1, 1], padding_list=[], name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  filter   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_input   A Tensor of type float32.  
  max_input   A Tensor of type float32.  
  min_filter   A Tensor of type float32.  
  max_filter   A Tensor of type float32.  
  min_freezed_output   A Tensor of type float32.  
  max_freezed_output   A Tensor of type float32.  
  strides   A list of ints.  
  padding   A string from: "SAME", "VALID".  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.qint8.  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1].  
  padding_list   An optional list of ints. Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, min_output, max_output).     output   A Tensor of type out_type.  
  min_output   A Tensor of type float32.  
  max_output   A Tensor of type float32.
tf.raw_ops.QuantizedConv2DWithBiasSignedSumAndReluAndRequantize  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DWithBiasSignedSumAndReluAndRequantize  
tf.raw_ops.QuantizedConv2DWithBiasSignedSumAndReluAndRequantize(
    input, filter, bias, min_input, max_input, min_filter, max_filter,
    min_freezed_output, max_freezed_output, summand, min_summand, max_summand,
    strides, padding, out_type=tf.dtypes.quint8, dilations=[1, 1, 1, 1],
    padding_list=[], name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  filter   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_input   A Tensor of type float32.  
  max_input   A Tensor of type float32.  
  min_filter   A Tensor of type float32.  
  max_filter   A Tensor of type float32.  
  min_freezed_output   A Tensor of type float32.  
  max_freezed_output   A Tensor of type float32.  
  summand   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  min_summand   A Tensor of type float32.  
  max_summand   A Tensor of type float32.  
  strides   A list of ints.  
  padding   A string from: "SAME", "VALID".  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8.  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1].  
  padding_list   An optional list of ints. Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, min_output, max_output).     output   A Tensor of type out_type.  
  min_output   A Tensor of type float32.  
  max_output   A Tensor of type float32.
tf.raw_ops.QuantizedConv2DWithBiasSumAndRelu  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DWithBiasSumAndRelu  
tf.raw_ops.QuantizedConv2DWithBiasSumAndRelu(
    input, filter, bias, min_input, max_input, min_filter, max_filter, summand,
    strides, padding, out_type=tf.dtypes.qint32, dilations=[1, 1, 1, 1],
    padding_list=[], name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  filter   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  bias   A Tensor of type float32.  
  min_input   A Tensor of type float32.  
  max_input   A Tensor of type float32.  
  min_filter   A Tensor of type float32.  
  max_filter   A Tensor of type float32.  
  summand   A Tensor of type float32.  
  strides   A list of ints.  
  padding   A string from: "SAME", "VALID".  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.qint32.  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1].  
  padding_list   An optional list of ints. Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, min_output, max_output).     output   A Tensor of type out_type.  
  min_output   A Tensor of type float32.  
  max_output   A Tensor of type float32.
tf.raw_ops.QuantizedConv2DWithBiasSumAndReluAndRequantize  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DWithBiasSumAndReluAndRequantize  
tf.raw_ops.QuantizedConv2DWithBiasSumAndReluAndRequantize(
    input, filter, bias, min_input, max_input, min_filter, max_filter,
    min_freezed_output, max_freezed_output, summand, min_summand, max_summand,
    strides, padding, out_type=tf.dtypes.quint8, dilations=[1, 1, 1, 1],
    padding_list=[], name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  filter   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_input   A Tensor of type float32.  
  max_input   A Tensor of type float32.  
  min_filter   A Tensor of type float32.  
  max_filter   A Tensor of type float32.  
  min_freezed_output   A Tensor of type float32.  
  max_freezed_output   A Tensor of type float32.  
  summand   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  min_summand   A Tensor of type float32.  
  max_summand   A Tensor of type float32.  
  strides   A list of ints.  
  padding   A string from: "SAME", "VALID".  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8.  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1].  
  padding_list   An optional list of ints. Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, min_output, max_output).     output   A Tensor of type out_type.  
  min_output   A Tensor of type float32.  
  max_output   A Tensor of type float32.
tf.raw_ops.QuantizedDepthwiseConv2D Computes quantized depthwise Conv2D.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedDepthwiseConv2D  
tf.raw_ops.QuantizedDepthwiseConv2D(
    input, filter, min_input, max_input, min_filter, max_filter, strides, padding,
    out_type=tf.dtypes.qint32, dilations=[1, 1, 1, 1], name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original input tensor.  
  filter   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original filter tensor.  
  min_input   A Tensor of type float32. The float value that the minimum quantized input value represents.  
  max_input   A Tensor of type float32. The float value that the maximum quantized input value represents.  
  min_filter   A Tensor of type float32. The float value that the minimum quantized filter value represents.  
  max_filter   A Tensor of type float32. The float value that the maximum quantized filter value represents.  
  strides   A list of ints. List of stride values.  
  padding   A string from: "SAME", "VALID".  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.qint32. The type of the output.  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1]. List of dilation values.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, min_output, max_output).     output   A Tensor of type out_type.  
  min_output   A Tensor of type float32.  
  max_output   A Tensor of type float32.
tf.raw_ops.QuantizedDepthwiseConv2DWithBias Computes quantized depthwise Conv2D with Bias.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedDepthwiseConv2DWithBias  
tf.raw_ops.QuantizedDepthwiseConv2DWithBias(
    input, filter, bias, min_input, max_input, min_filter, max_filter, strides,
    padding, out_type=tf.dtypes.qint32, dilations=[1, 1, 1, 1], name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original input tensor.  
  filter   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original filter tensor.  
  bias   A Tensor of type float32. The original bias tensor.  
  min_input   A Tensor of type float32. The float value that the minimum quantized input value represents.  
  max_input   A Tensor of type float32. The float value that the maximum quantized input value represents.  
  min_filter   A Tensor of type float32. The float value that the minimum quantized filter value represents.  
  max_filter   A Tensor of type float32. The float value that the maximum quantized filter value represents.  
  strides   A list of ints. List of stride values.  
  padding   A string from: "SAME", "VALID".  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.qint32. The type of the output.  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1]. List of dilation values.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, min_output, max_output).     output   A Tensor of type out_type.  
  min_output   A Tensor of type float32.  
  max_output   A Tensor of type float32.
tf.raw_ops.QuantizedDepthwiseConv2DWithBiasAndRelu Computes quantized depthwise Conv2D with Bias and Relu.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedDepthwiseConv2DWithBiasAndRelu  
tf.raw_ops.QuantizedDepthwiseConv2DWithBiasAndRelu(
    input, filter, bias, min_input, max_input, min_filter, max_filter, strides,
    padding, out_type=tf.dtypes.qint32, dilations=[1, 1, 1, 1], padding_list=[],
    name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original input tensor.  
  filter   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original filter tensor.  
  bias   A Tensor of type float32. The original bias tensor.  
  min_input   A Tensor of type float32. The float value that the minimum quantized input value represents.  
  max_input   A Tensor of type float32. The float value that the maximum quantized input value represents.  
  min_filter   A Tensor of type float32. The float value that the minimum quantized filter value represents.  
  max_filter   A Tensor of type float32. The float value that the maximum quantized filter value represents.  
  strides   A list of ints. List of stride values.  
  padding   A string from: "SAME", "VALID".  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.qint32. The type of the output.  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1]. List of dilation values.  
  padding_list   An optional list of ints. Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, min_output, max_output).     output   A Tensor of type out_type.  
  min_output   A Tensor of type float32.  
  max_output   A Tensor of type float32.
tf.raw_ops.QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize Computes quantized depthwise Conv2D with Bias, Relu and Requantize.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize  
tf.raw_ops.QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize(
    input, filter, bias, min_input, max_input, min_filter, max_filter,
    min_freezed_output, max_freezed_output, strides, padding,
    out_type=tf.dtypes.quint8, dilations=[1, 1, 1, 1], padding_list=[], name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original input tensor.  
  filter   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. The original filter tensor.  
  bias   A Tensor. Must be one of the following types: float32, qint32. The original bias tensor.  
  min_input   A Tensor of type float32. The float value that the minimum quantized input value represents.  
  max_input   A Tensor of type float32. The float value that the maximum quantized input value represents.  
  min_filter   A Tensor of type float32. The float value that the minimum quantized filter value represents.  
  max_filter   A Tensor of type float32. The float value that the maximum quantized filter value represents.  
  min_freezed_output   A Tensor of type float32. The minimum float value of the output tensor.  
  max_freezed_output   A Tensor of type float32. The maximum float value of the output tensor.  
  strides   A list of ints. List of stride values.  
  padding   A string from: "SAME", "VALID".  
  out_type   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8. The type of the output.  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1]. List of dilation values.  
  padding_list   An optional list of ints. Defaults to [].  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, min_output, max_output).     output   A Tensor of type out_type.  
  min_output   A Tensor of type float32.  
  max_output   A Tensor of type float32.
tf.raw_ops.QuantizedInstanceNorm Quantized Instance normalization.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedInstanceNorm  
tf.raw_ops.QuantizedInstanceNorm(
    x, x_min, x_max, output_range_given=False, given_y_min=0, given_y_max=0,
    variance_epsilon=1e-05, min_separation=0.001, name=None
)

 


 Args
  x   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. A 4D input Tensor.  
  x_min   A Tensor of type float32. The value represented by the lowest quantized input.  
  x_max   A Tensor of type float32. The value represented by the highest quantized input.  
  output_range_given   An optional bool. Defaults to False. If True, given_y_min and given_y_min and given_y_max are used as the output range. Otherwise, the implementation computes the output range.  
  given_y_min   An optional float. Defaults to 0. Output in y_min if output_range_given is True.  
  given_y_max   An optional float. Defaults to 0. Output in y_max if output_range_given is True.  
  variance_epsilon   An optional float. Defaults to 1e-05. A small float number to avoid dividing by 0.  
  min_separation   An optional float. Defaults to 0.001. Minimum value of y_max - y_min  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (y, y_min, y_max).     y   A Tensor. Has the same type as x.  
  y_min   A Tensor of type float32.  
  y_max   A Tensor of type float32.
tf.raw_ops.QuantizedMatMul Perform a quantized matrix multiplication of a by the matrix b.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedMatMul  
tf.raw_ops.QuantizedMatMul(
    a, b, min_a, max_a, min_b, max_b, Toutput=tf.dtypes.qint32, transpose_a=False,
    transpose_b=False, Tactivation=tf.dtypes.quint8, name=None
)
 The inputs must be two-dimensional matrices 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).
 


 Args
  a   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. Must be a two-dimensional tensor.  
  b   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. Must be a two-dimensional tensor.  
  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.  
  Toutput   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.qint32.  
  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.  
  Tactivation   An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8. The type of output produced by activation function following this operation.  
  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.QuantizedMatMulWithBias Performs 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.QuantizedMatMulWithBias  
tf.raw_ops.QuantizedMatMulWithBias(
    a, b, bias, min_a, max_a, min_b, max_b, Toutput=tf.dtypes.qint32,
    transpose_a=False, transpose_b=False, input_quant_mode='MIN_FIRST',
    name=None
)
 add. 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. 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 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. Toutput: An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.qint32. 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.QuantizedMatMulWithBiasAndDequantize  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedMatMulWithBiasAndDequantize  
tf.raw_ops.QuantizedMatMulWithBiasAndDequantize(
    a, b, bias, min_a, max_a, min_b, max_b, min_freezed_output, max_freezed_output,
    Toutput, 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   A tf.DType from: tf.float32.  
  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 Tensor of type Toutput.
tf.raw_ops.QuantizedMatMulWithBiasAndRelu 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.QuantizedMatMulWithBiasAndRelu  
tf.raw_ops.QuantizedMatMulWithBiasAndRelu(
    a, b, bias, min_a, max_a, min_b, max_b, Toutput=tf.dtypes.qint32,
    transpose_a=False, transpose_b=False, input_quant_mode='MIN_FIRST',
    name=None
)
 add and relu 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. 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 of type float32. 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. Toutput: An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.qint32. 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`.