neulab/docprompting-conala huggingface dataset:
tf.raw_ops.Conv2D Computes a 2-D convolution given 4-D input and filter tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Conv2D  
tf.raw_ops.Conv2D(
    input, filter, strides, padding, use_cudnn_on_gpu=True, explicit_paddings=[],
    data_format='NHWC', dilations=[1, 1, 1, 1], name=None
)
 Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:  Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels]. Extracts image patches from the input tensor to form a virtual tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels]. For each patch, right-multiplies the filter matrix and the image patch vector.  In detail, with the default NHWC format, output[b, i, j, k] =
    sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
                    filter[di, dj, q, k]
 Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64, int32. A 4-D tensor. The dimension order is interpreted according to the value of data_format, see below for details.  
  filter   A Tensor. Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]  
  strides   A list of ints. 1-D tensor of length 4. The stride of the sliding window for each dimension of input. The dimension order is determined by the value of data_format, see below for details.  
  padding   A string from: "SAME", "VALID", "EXPLICIT". The type of padding algorithm to use.  
  use_cudnn_on_gpu   An optional bool. Defaults to True.  
  explicit_paddings   An optional list of ints. Defaults to []. If padding is "EXPLICIT", the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is explicit_paddings[2 * i] and explicit_paddings[2 * i + 1], respectively. If padding is not "EXPLICIT", explicit_paddings must be empty.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].  
  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 Tensor. Has the same type as input.
tf.raw_ops.Conv2DBackpropFilter Computes the gradients of convolution with respect to the filter.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Conv2DBackpropFilter  
tf.raw_ops.Conv2DBackpropFilter(
    input, filter_sizes, out_backprop, strides, padding, use_cudnn_on_gpu=True,
    explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1],
    name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].  
  filter_sizes   A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 4-D [filter_height, filter_width, in_channels, out_channels] tensor.  
  out_backprop   A Tensor. Must have the same type as input. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.  
  strides   A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.  
  padding   A string from: "SAME", "VALID", "EXPLICIT". The type of padding algorithm to use.  
  use_cudnn_on_gpu   An optional bool. Defaults to True.  
  explicit_paddings   An optional list of ints. Defaults to []. If padding is "EXPLICIT", the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is explicit_paddings[2 * i] and explicit_paddings[2 * i + 1], respectively. If padding is not "EXPLICIT", explicit_paddings must be empty.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].  
  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 Tensor. Has the same type as input.
tf.raw_ops.Conv2DBackpropInput Computes the gradients of convolution with respect to the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Conv2DBackpropInput  
tf.raw_ops.Conv2DBackpropInput(
    input_sizes, filter, out_backprop, strides, padding, use_cudnn_on_gpu=True,
    explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1],
    name=None
)

 


 Args
  input_sizes   A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.  
  filter   A Tensor. Must be one of the following types: half, bfloat16, float32, float64, int32. 4-D with shape [filter_height, filter_width, in_channels, out_channels].  
  out_backprop   A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]. Gradients w.r.t. the output of the convolution.  
  strides   A list of ints. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.  
  padding   A string from: "SAME", "VALID", "EXPLICIT". The type of padding algorithm to use.  
  use_cudnn_on_gpu   An optional bool. Defaults to True.  
  explicit_paddings   An optional list of ints. Defaults to []. If padding is "EXPLICIT", the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is explicit_paddings[2 * i] and explicit_paddings[2 * i + 1], respectively. If padding is not "EXPLICIT", explicit_paddings must be empty.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].  
  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 Tensor. Has the same type as filter.
tf.raw_ops.Conv3D Computes a 3-D convolution given 5-D input and filter tensors.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Conv3D  
tf.raw_ops.Conv3D(
    input, filter, strides, padding, data_format='NDHWC', dilations=[1, 1,
    1, 1, 1], name=None
)
 In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or sliding inner-product. Our Conv3D implements a form of cross-correlation.
 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Shape [batch, in_depth, in_height, in_width, in_channels].  
  filter   A Tensor. Must have the same type as input. Shape [filter_depth, filter_height, filter_width, in_channels, out_channels]. in_channels must match between input and filter.  
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1, 1]. 1-D tensor of length 5. 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 Tensor. Has the same type as input.
tf.raw_ops.Conv3DBackpropFilter Computes the gradients of 3-D convolution with respect to the filter.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Conv3DBackpropFilter  
tf.raw_ops.Conv3DBackpropFilter(
    input, filter, out_backprop, strides, padding, dilations=[1, 1, 1, 1, 1],
    name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: half, float32, float64. Shape [batch, depth, rows, cols, in_channels].  
  filter   A Tensor. Must have the same type as input. Shape [depth, rows, cols, in_channels, out_channels]. in_channels must match between input and filter.  
  out_backprop   A Tensor. Must have the same type as input. Backprop signal of shape [batch, out_depth, out_rows, out_cols, out_channels].  
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1, 1].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Conv3DBackpropFilterV2 Computes the gradients of 3-D convolution with respect to the filter.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Conv3DBackpropFilterV2  
tf.raw_ops.Conv3DBackpropFilterV2(
    input, filter_sizes, out_backprop, strides, padding,
    data_format='NDHWC', dilations=[1, 1, 1, 1, 1], name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Shape [batch, depth, rows, cols, in_channels].  
  filter_sizes   A Tensor of type int32. An integer vector representing the tensor shape of filter, where filter is a 5-D [filter_depth, filter_height, filter_width, in_channels, out_channels] tensor.  
  out_backprop   A Tensor. Must have the same type as input. Backprop signal of shape [batch, out_depth, out_rows, out_cols, out_channels].  
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1, 1]. 1-D tensor of length 5. 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 Tensor. Has the same type as input.
tf.raw_ops.Conv3DBackpropInput Computes the gradients of 3-D convolution with respect to the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Conv3DBackpropInput  
tf.raw_ops.Conv3DBackpropInput(
    input, filter, out_backprop, strides, padding, dilations=[1, 1, 1, 1, 1],
    name=None
)

 


 Args
  input   A Tensor. Must be one of the following types: half, float32, float64. Shape [batch, depth, rows, cols, in_channels].  
  filter   A Tensor. Must have the same type as input. Shape [depth, rows, cols, in_channels, out_channels]. in_channels must match between input and filter.  
  out_backprop   A Tensor. Must have the same type as input. Backprop signal of shape [batch, out_depth, out_rows, out_cols, out_channels].  
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1, 1].  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Conv3DBackpropInputV2 Computes the gradients of 3-D convolution with respect to the input.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Conv3DBackpropInputV2  
tf.raw_ops.Conv3DBackpropInputV2(
    input_sizes, filter, out_backprop, strides, padding,
    data_format='NDHWC', dilations=[1, 1, 1, 1, 1], name=None
)

 


 Args
  input_sizes   A Tensor. Must be one of the following types: int32, int64. An integer vector representing the tensor shape of input, where input is a 5-D [batch, depth, rows, cols, in_channels] tensor.  
  filter   A Tensor. Must be one of the following types: half, bfloat16, float32, float64. Shape [depth, rows, cols, in_channels, out_channels]. in_channels must match between input and filter.  
  out_backprop   A Tensor. Must have the same type as filter. Backprop signal of shape [batch, out_depth, out_rows, out_cols, out_channels].  
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].  
  dilations   An optional list of ints. Defaults to [1, 1, 1, 1, 1]. 1-D tensor of length 5. 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 Tensor. Has the same type as filter.
tf.raw_ops.Copy Copy a tensor from CPU-to-CPU or GPU-to-GPU.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Copy  
tf.raw_ops.Copy(
    input, tensor_name='', debug_ops_spec=[], name=None
)
 Performs CPU-to-CPU or GPU-to-GPU deep-copying of tensor, depending on the device on which the tensor is allocated. N.B.: If the all downstream attached debug ops are disabled given the current gRPC gating status, the output will simply forward the input tensor without deep-copying. See the documentation of Debug* ops for more details. Unlike the CopyHost Op, this op does not have HostMemory constraint on its input or output.
 


 Args
  input   A Tensor. Input tensor.  
  tensor_name   An optional string. Defaults to "". The name of the input tensor.  
  debug_ops_spec   An optional list of strings. Defaults to []. A list of debug op spec (op, url, gated_grpc) for attached debug ops. Each element of the list has the format ;;, wherein gated_grpc is boolean represented as 0/1. E.g., "DebugIdentity;grpc://foo:3333;1", "DebugIdentity;file:///tmp/tfdbg_1;0". 
 
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.CopyHost Copy a tensor to host.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CopyHost  
tf.raw_ops.CopyHost(
    input, tensor_name='', debug_ops_spec=[], name=None
)
 Performs CPU-to-CPU deep-copying of tensor. N.B.: If the all downstream attached debug ops are disabled given the current gRPC gating status, the output will simply forward the input tensor without deep-copying. See the documentation of Debug* ops for more details. Unlike the Copy Op, this op has HostMemory constraint on its input or output.
 


 Args
  input   A Tensor. Input tensor.  
  tensor_name   An optional string. Defaults to "". The name of the input tensor.  
  debug_ops_spec   An optional list of strings. Defaults to []. A list of debug op spec (op, url, gated_grpc) for attached debug ops. Each element of the list has the format ;;, wherein gated_grpc is boolean represented as 0/1. E.g., "DebugIdentity;grpc://foo:3333;1", "DebugIdentity;file:///tmp/tfdbg_1;0". 
 
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Cos Computes cos of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Cos  
tf.raw_ops.Cos(
    x, name=None
)
 Given an input tensor, this function computes cosine of every element in the tensor. Input range is (-inf, inf) and output range is [-1,1]. If input lies outside the boundary, nan is returned. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")])
tf.math.cos(x) ==> [nan -0.91113025 0.87758255 0.5403023 0.36235774 0.48718765 -0.95215535 nan]

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.Cosh Computes hyperbolic cosine of x element-wise.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Cosh  
tf.raw_ops.Cosh(
    x, name=None
)
 Given an input tensor, this function computes hyperbolic cosine of every element in the tensor. Input range is [-inf, inf] and output range is [1, inf]. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])
tf.math.cosh(x) ==> [inf 4.0515420e+03 1.1276259e+00 1.5430807e+00 1.8106556e+00 3.7621956e+00 1.1013233e+04 inf]

 


 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.CountUpTo Increments 'ref' until it reaches 'limit'.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CountUpTo  
tf.raw_ops.CountUpTo(
    ref, limit, name=None
)

 


 Args
  ref   A mutable Tensor. Must be one of the following types: int32, int64. Should be from a scalar Variable node.  
  limit   An int. If incrementing ref would bring it above limit, instead generates an 'OutOfRange' error.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as ref.
tf.raw_ops.CreateSummaryDbWriter  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CreateSummaryDbWriter  
tf.raw_ops.CreateSummaryDbWriter(
    writer, db_uri, experiment_name, run_name, user_name, name=None
)

 


 Args
  writer   A Tensor of type resource.  
  db_uri   A Tensor of type string.  
  experiment_name   A Tensor of type string.  
  run_name   A Tensor of type string.  
  user_name   A Tensor of type string.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.
tf.raw_ops.CreateSummaryFileWriter  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.CreateSummaryFileWriter  
tf.raw_ops.CreateSummaryFileWriter(
    writer, logdir, max_queue, flush_millis, filename_suffix, name=None
)

 


 Args
  writer   A Tensor of type resource.  
  logdir   A Tensor of type string.  
  max_queue   A Tensor of type int32.  
  flush_millis   A Tensor of type int32.  
  filename_suffix   A Tensor of type string.  
  name   A name for the operation (optional).   
 


 Returns   The created Operation.