neulab/docprompting-conala huggingface dataset:
tf.raw_ops.QuantizeAndDequantizeV4 Returns the gradient of QuantizeAndDequantizeV4.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizeAndDequantizeV4  
tf.raw_ops.QuantizeAndDequantizeV4(
    input, input_min, input_max, signed_input=True, num_bits=8, range_given=False,
    round_mode='HALF_TO_EVEN', narrow_range=False, axis=-1, name=None
)
 This is almost identical to QuantizeAndDequantizeV2, except that it returns a gradient of 1 for inputs that are within the quantization range, or 0 otherwise.
 


 Args
  input   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  input_min   A Tensor. Must have the same type as input.  
  input_max   A Tensor. Must have the same type as input.  
  signed_input   An optional bool. Defaults to True.  
  num_bits   An optional int. Defaults to 8.  
  range_given   An optional bool. Defaults to False.  
  round_mode   An optional string from: "HALF_TO_EVEN", "HALF_UP". Defaults to "HALF_TO_EVEN".  
  narrow_range   An optional bool. Defaults to False.  
  axis   An optional int. Defaults to -1.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.QuantizeAndDequantizeV4Grad Returns the gradient of QuantizeAndDequantizeV4.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizeAndDequantizeV4Grad  
tf.raw_ops.QuantizeAndDequantizeV4Grad(
    gradients, input, input_min, input_max, axis=-1, name=None
)
 Returns a gradient of 1 for inputs that are within the quantization range, or 0 otherwise.
 


 Args
  gradients   A Tensor. Must be one of the following types: bfloat16, half, float32, float64.  
  input   A Tensor. Must have the same type as gradients.  
  input_min   A Tensor. Must have the same type as gradients.  
  input_max   A Tensor. Must have the same type as gradients.  
  axis   An optional int. Defaults to -1.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (input_backprop, input_min_backprop, input_max_backprop).     input_backprop   A Tensor. Has the same type as gradients.  
  input_min_backprop   A Tensor. Has the same type as gradients.  
  input_max_backprop   A Tensor. Has the same type as gradients.
tf.raw_ops.QuantizedAdd 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.QuantizedAdd  
tf.raw_ops.QuantizedAdd(
    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.QuantizedAvgPool Produces the average 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.QuantizedAvgPool  
tf.raw_ops.QuantizedAvgPool(
    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. 4-D with shape [batch, height, width, channels].  
  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.QuantizedBatchNormWithGlobalNormalization Quantized Batch normalization.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedBatchNormWithGlobalNormalization  
tf.raw_ops.QuantizedBatchNormWithGlobalNormalization(
    t, t_min, t_max, m, m_min, m_max, v, v_min, v_max, beta, beta_min, beta_max,
    gamma, gamma_min, gamma_max, out_type, variance_epsilon,
    scale_after_normalization, name=None
)
 This op is deprecated and will be removed in the future. Prefer tf.nn.batch_normalization.
 


 Args
  t   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. A 4D input Tensor.  
  t_min   A Tensor of type float32. The value represented by the lowest quantized input.  
  t_max   A Tensor of type float32. The value represented by the highest quantized input.  
  m   A Tensor. Must have the same type as t. A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof.  
  m_min   A Tensor of type float32. The value represented by the lowest quantized mean.  
  m_max   A Tensor of type float32. The value represented by the highest quantized mean.  
  v   A Tensor. Must have the same type as t. A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof.  
  v_min   A Tensor of type float32. The value represented by the lowest quantized variance.  
  v_max   A Tensor of type float32. The value represented by the highest quantized variance.  
  beta   A Tensor. Must have the same type as t. A 1D beta Tensor with size matching the last dimension of t. An offset to be added to the normalized tensor.  
  beta_min   A Tensor of type float32. The value represented by the lowest quantized offset.  
  beta_max   A Tensor of type float32. The value represented by the highest quantized offset.  
  gamma   A Tensor. Must have the same type as t. A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this tensor will be multiplied with the normalized tensor.  
  gamma_min   A Tensor of type float32. The value represented by the lowest quantized gamma.  
  gamma_max   A Tensor of type float32. The value represented by the highest quantized gamma.  
  out_type   A tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16.  
  variance_epsilon   A float. A small float number to avoid dividing by 0.  
  scale_after_normalization   A bool. A bool indicating whether the resulted tensor needs to be multiplied with gamma.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (result, result_min, result_max).     result   A Tensor of type out_type.  
  result_min   A Tensor of type float32.  
  result_max   A Tensor of type float32.
tf.raw_ops.QuantizedBiasAdd Adds Tensor 'bias' to Tensor 'input' for Quantized types.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedBiasAdd  
tf.raw_ops.QuantizedBiasAdd(
    input, bias, min_input, max_input, min_bias, max_bias, out_type, name=None
)
 Broadcasts the values of bias on dimensions 0..N-2 of 'input'.
 


 Args
  input   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  bias   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16. A 1D bias Tensor with size matching the last dimension of 'input'.  
  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.  
  min_bias   A Tensor of type float32. The float value that the lowest quantized bias value represents.  
  max_bias   A Tensor of type float32. The float value that the highest quantized bias value represents.  
  out_type   A tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, min_out, max_out).     output   A Tensor of type out_type.  
  min_out   A Tensor of type float32.  
  max_out   A Tensor of type float32.
tf.raw_ops.QuantizedConcat Concatenates quantized tensors along one dimension.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConcat  
tf.raw_ops.QuantizedConcat(
    concat_dim, values, input_mins, input_maxes, name=None
)

 


 Args
  concat_dim   A Tensor of type int32. 0-D. The dimension along which to concatenate. Must be in the range [0, rank(values)).  
  values   A list of at least 2 Tensor objects with the same type. The N Tensors to concatenate. Their ranks and types must match, and their sizes must match in all dimensions except concat_dim.  
  input_mins   A list with the same length as values of Tensor objects with type float32. The minimum scalar values for each of the input tensors.  
  input_maxes   A list with the same length as values of Tensor objects with type float32. The maximum scalar values for each of the input tensors.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (output, output_min, output_max).     output   A Tensor. Has the same type as values.  
  output_min   A Tensor of type float32.  
  output_max   A Tensor of type float32.
tf.raw_ops.QuantizedConv2D Computes a 2D convolution given quantized 4D input and filter tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2D  
tf.raw_ops.QuantizedConv2D(
    input, filter, min_input, max_input, min_filter, max_filter, strides, padding,
    out_type=tf.dtypes.qint32, dilations=[1, 1, 1, 1], name=None
)
 The inputs are quantized tensors where the lowest value represents the real number of the associated minimum, and the highest represents the maximum. This means that you can only interpret the quantized output in the same way, by taking the returned minimum and maximum values into account.
 


 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. filter's input_depth dimension must match input's depth dimensions.  
  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.  
  min_filter   A Tensor of type float32. The float value that the lowest quantized filter value represents.  
  max_filter   A Tensor of type float32. The float value that the highest quantized filter value represents.  
  strides   A list of ints. The stride of the sliding window for each dimension of the input tensor.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  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]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.  
  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.QuantizedConv2DAndRelu  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DAndRelu  
tf.raw_ops.QuantizedConv2DAndRelu(
    input, filter, 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.  
  filter   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  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.  
  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.QuantizedConv2DAndReluAndRequantize  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DAndReluAndRequantize  
tf.raw_ops.QuantizedConv2DAndReluAndRequantize(
    input, filter, 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.  
  filter   A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.  
  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.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.QuantizedConv2DAndRequantize  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DAndRequantize  
tf.raw_ops.QuantizedConv2DAndRequantize(
    input, filter, 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.  
  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.QuantizedConv2DPerChannel Computes QuantizedConv2D per channel.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DPerChannel  
tf.raw_ops.QuantizedConv2DPerChannel(
    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 minimum value of the input tensor  
  max_input   A Tensor of type float32. The maximum value of the input tensor.  
  min_filter   A Tensor of type float32. The minimum value of the filter tensor.  
  max_filter   A Tensor of type float32. The maximum value of the filter 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.qint32. The quantized type of output tensor that needs to be converted.  
  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.QuantizedConv2DWithBias  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DWithBias  
tf.raw_ops.QuantizedConv2DWithBias(
    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.  
  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.  
  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.QuantizedConv2DWithBiasAndRelu  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DWithBiasAndRelu  
tf.raw_ops.QuantizedConv2DWithBiasAndRelu(
    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.  
  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.  
  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.QuantizedConv2DWithBiasAndReluAndRequantize  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DWithBiasAndReluAndRequantize  
tf.raw_ops.QuantizedConv2DWithBiasAndReluAndRequantize(
    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.  
  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.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.