neulab/docprompting-conala huggingface dataset:
tf.raw_ops.FractionalMaxPoolGrad Computes gradient of the FractionalMaxPool function.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FractionalMaxPoolGrad  
tf.raw_ops.FractionalMaxPoolGrad(
    orig_input, orig_output, out_backprop, row_pooling_sequence,
    col_pooling_sequence, overlapping=False, name=None
)

 


 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, int64. Original input for fractional_max_pool  
  orig_output   A Tensor. Must have the same type as orig_input. Original output for fractional_max_pool  
  out_backprop   A Tensor. Must have the same type as orig_input. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the output of fractional_max_pool.  
  row_pooling_sequence   A Tensor of type int64. row pooling sequence, form pooling region with col_pooling_sequence.  
  col_pooling_sequence   A Tensor of type int64. column pooling sequence, form pooling region with row_pooling sequence.  
  overlapping   An optional bool. Defaults to False. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: index 0 1 2 3 4 value 20 5 16 3 7 If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional max pooling. 
 
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as orig_input.
tf.raw_ops.FresnelCos  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FresnelCos  
tf.raw_ops.FresnelCos(
    x, name=None
)

 


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


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.FresnelSin  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FresnelSin  
tf.raw_ops.FresnelSin(
    x, name=None
)

 


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


 Returns   A Tensor. Has the same type as x.
tf.raw_ops.FusedBatchNorm Batch normalization.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNorm  
tf.raw_ops.FusedBatchNorm(
    x, scale, offset, mean, variance, epsilon=0.0001, exponential_avg_factor=1,
    data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
 


 Args
  x   A Tensor. Must be one of the following types: float32. A 4D Tensor for input data.  
  scale   A Tensor. Must have the same type as x. A 1D Tensor for scaling factor, to scale the normalized x.  
  offset   A Tensor. Must have the same type as x. A 1D Tensor for offset, to shift to the normalized x.  
  mean   A Tensor. Must have the same type as x. A 1D Tensor for population mean. Used for inference only; must be empty for training.  
  variance   A Tensor. Must have the same type as x. A 1D Tensor for population variance. Used for inference only; must be empty for training.  
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.  
  exponential_avg_factor   An optional float. Defaults to 1.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for x and y. Either "NHWC" (default) or "NCHW".  
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2).     y   A Tensor. Has the same type as x.  
  batch_mean   A Tensor. Has the same type as x.  
  batch_variance   A Tensor. Has the same type as x.  
  reserve_space_1   A Tensor. Has the same type as x.  
  reserve_space_2   A Tensor. Has the same type as x.
tf.raw_ops.FusedBatchNormGrad Gradient for batch normalization.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormGrad  
tf.raw_ops.FusedBatchNormGrad(
    y_backprop, x, scale, reserve_space_1, reserve_space_2, epsilon=0.0001,
    data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
 


 Args
  y_backprop   A Tensor. Must be one of the following types: float32. A 4D Tensor for the gradient with respect to y.  
  x   A Tensor. Must have the same type as y_backprop. A 4D Tensor for input data.  
  scale   A Tensor. Must have the same type as y_backprop. A 1D Tensor for scaling factor, to scale the normalized x.  
  reserve_space_1   A Tensor. Must have the same type as y_backprop. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation.  
  reserve_space_2   A Tensor. Must have the same type as y_backprop. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation.  
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW".  
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (x_backprop, scale_backprop, offset_backprop, reserve_space_3, reserve_space_4).     x_backprop   A Tensor. Has the same type as y_backprop.  
  scale_backprop   A Tensor. Has the same type as y_backprop.  
  offset_backprop   A Tensor. Has the same type as y_backprop.  
  reserve_space_3   A Tensor. Has the same type as y_backprop.  
  reserve_space_4   A Tensor. Has the same type as y_backprop.
tf.raw_ops.FusedBatchNormGradV2 Gradient for batch normalization.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormGradV2  
tf.raw_ops.FusedBatchNormGradV2(
    y_backprop, x, scale, reserve_space_1, reserve_space_2, epsilon=0.0001,
    data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
 


 Args
  y_backprop   A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for the gradient with respect to y.  
  x   A Tensor. Must have the same type as y_backprop. A 4D Tensor for input data.  
  scale   A Tensor of type float32. A 1D Tensor for scaling factor, to scale the normalized x.  
  reserve_space_1   A Tensor. Must be one of the following types: float32. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation.  
  reserve_space_2   A Tensor. Must have the same type as reserve_space_1. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation.  
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW".  
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (x_backprop, scale_backprop, offset_backprop, reserve_space_3, reserve_space_4).     x_backprop   A Tensor. Has the same type as y_backprop.  
  scale_backprop   A Tensor. Has the same type as reserve_space_1.  
  offset_backprop   A Tensor. Has the same type as reserve_space_1.  
  reserve_space_3   A Tensor. Has the same type as reserve_space_1.  
  reserve_space_4   A Tensor. Has the same type as reserve_space_1.
tf.raw_ops.FusedBatchNormGradV3 Gradient for batch normalization.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormGradV3  
tf.raw_ops.FusedBatchNormGradV3(
    y_backprop, x, scale, reserve_space_1, reserve_space_2, reserve_space_3,
    epsilon=0.0001, data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
 


 Args
  y_backprop   A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for the gradient with respect to y.  
  x   A Tensor. Must have the same type as y_backprop. A 4D Tensor for input data.  
  scale   A Tensor of type float32. A 1D Tensor for scaling factor, to scale the normalized x.  
  reserve_space_1   A Tensor. Must be one of the following types: float32. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation.  
  reserve_space_2   A Tensor. Must have the same type as reserve_space_1. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation.  
  reserve_space_3   A Tensor. Must have the same type as reserve_space_1. When is_training is True, a 1D Tensor for some intermediate results to be reused in gradient computation. When is_training is False, a dummy empty Tensor will be created.  
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.  
  data_format   An optional string from: "NHWC", "NCHW", "NDHWC", "NCDHW". Defaults to "NHWC". The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW".  
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (x_backprop, scale_backprop, offset_backprop, reserve_space_4, reserve_space_5).     x_backprop   A Tensor. Has the same type as y_backprop.  
  scale_backprop   A Tensor. Has the same type as reserve_space_1.  
  offset_backprop   A Tensor. Has the same type as reserve_space_1.  
  reserve_space_4   A Tensor. Has the same type as reserve_space_1.  
  reserve_space_5   A Tensor. Has the same type as reserve_space_1.
tf.raw_ops.FusedBatchNormV2 Batch normalization.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormV2  
tf.raw_ops.FusedBatchNormV2(
    x, scale, offset, mean, variance, epsilon=0.0001, exponential_avg_factor=1,
    data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
 


 Args
  x   A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for input data.  
  scale   A Tensor. Must be one of the following types: float32. A 1D Tensor for scaling factor, to scale the normalized x.  
  offset   A Tensor. Must have the same type as scale. A 1D Tensor for offset, to shift to the normalized x.  
  mean   A Tensor. Must have the same type as scale. A 1D Tensor for population mean. Used for inference only; must be empty for training.  
  variance   A Tensor. Must have the same type as scale. A 1D Tensor for population variance. Used for inference only; must be empty for training.  
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.  
  exponential_avg_factor   An optional float. Defaults to 1.  
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". The data format for x and y. Either "NHWC" (default) or "NCHW".  
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2).     y   A Tensor. Has the same type as x.  
  batch_mean   A Tensor. Has the same type as scale.  
  batch_variance   A Tensor. Has the same type as scale.  
  reserve_space_1   A Tensor. Has the same type as scale.  
  reserve_space_2   A Tensor. Has the same type as scale.
tf.raw_ops.FusedBatchNormV3 Batch normalization.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FusedBatchNormV3  
tf.raw_ops.FusedBatchNormV3(
    x, scale, offset, mean, variance, epsilon=0.0001, exponential_avg_factor=1,
    data_format='NHWC', is_training=True, name=None
)
 Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors.
 


 Args
  x   A Tensor. Must be one of the following types: half, bfloat16, float32. A 4D Tensor for input data.  
  scale   A Tensor. Must be one of the following types: float32. A 1D Tensor for scaling factor, to scale the normalized x.  
  offset   A Tensor. Must have the same type as scale. A 1D Tensor for offset, to shift to the normalized x.  
  mean   A Tensor. Must have the same type as scale. A 1D Tensor for population mean. Used for inference only; must be empty for training.  
  variance   A Tensor. Must have the same type as scale. A 1D Tensor for population variance. Used for inference only; must be empty for training.  
  epsilon   An optional float. Defaults to 0.0001. A small float number added to the variance of x.  
  exponential_avg_factor   An optional float. Defaults to 1.  
  data_format   An optional string from: "NHWC", "NCHW", "NDHWC", "NCDHW". Defaults to "NHWC". The data format for x and y. Either "NHWC" (default) or "NCHW".  
  is_training   An optional bool. Defaults to True. A bool value to indicate the operation is for training (default) or inference.  
  name   A name for the operation (optional).   
 


 Returns   A tuple of Tensor objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2, reserve_space_3).     y   A Tensor. Has the same type as x.  
  batch_mean   A Tensor. Has the same type as scale.  
  batch_variance   A Tensor. Has the same type as scale.  
  reserve_space_1   A Tensor. Has the same type as scale.  
  reserve_space_2   A Tensor. Has the same type as scale.  
  reserve_space_3   A Tensor. Has the same type as scale.
tf.raw_ops.FusedPadConv2D Performs a padding as a preprocess during a convolution.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FusedPadConv2D  
tf.raw_ops.FusedPadConv2D(
    input, paddings, filter, mode, strides, padding, name=None
)
 Similar to FusedResizeAndPadConv2d, this op allows for an optimized implementation where the spatial padding transformation stage is fused with the im2col lookup, but in this case without the bilinear filtering required for resizing. Fusing the padding prevents the need to write out the intermediate results as whole tensors, reducing memory pressure, and we can get some latency gains by merging the transformation calculations. The data_format attribute for Conv2D isn't supported by this op, and 'NHWC' order is used instead. Internally this op uses a single per-graph scratch buffer, which means that it will block if multiple versions are being run in parallel. This is because this operator is primarily an optimization to minimize memory usage.
 


 Args
  input   A Tensor. Must be one of the following types: half, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].  
  paddings   A Tensor of type int32. A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of input.  
  filter   A Tensor. Must have the same type as input. 4-D with shape [filter_height, filter_width, in_channels, out_channels].  
  mode   A string from: "REFLECT", "SYMMETRIC".  
  strides   A list of ints. 1-D of length 4. The stride of the sliding window for each dimension of input. Must be in the same order as the dimension specified with format.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.FusedResizeAndPadConv2D Performs a resize and padding as a preprocess during a convolution.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.FusedResizeAndPadConv2D  
tf.raw_ops.FusedResizeAndPadConv2D(
    input, size, paddings, filter, mode, strides, padding,
    resize_align_corners=False, name=None
)
 It's often possible to do spatial transformations more efficiently as part of the packing stage of a convolution, so this op allows for an optimized implementation where these stages are fused together. This prevents the need to write out the intermediate results as whole tensors, reducing memory pressure, and we can get some latency gains by merging the transformation calculations. The data_format attribute for Conv2D isn't supported by this op, and defaults to 'NHWC' order. Internally this op uses a single per-graph scratch buffer, which means that it will block if multiple versions are being run in parallel. This is because this operator is primarily an optimization to minimize memory usage.
 


 Args
  input   A Tensor. Must be one of the following types: half, float32, float64. 4-D with shape [batch, in_height, in_width, in_channels].  
  size   A Tensor of type int32. A 1-D int32 Tensor of 2 elements: new_height, new_width. The new size for the images.  
  paddings   A Tensor of type int32. A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of input.  
  filter   A Tensor. Must have the same type as input. 4-D with shape [filter_height, filter_width, in_channels, out_channels].  
  mode   A string from: "REFLECT", "SYMMETRIC".  
  strides   A list of ints. 1-D of length 4. The stride of the sliding window for each dimension of input. Must be in the same order as the dimension specified with format.  
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.  
  resize_align_corners   An optional bool. Defaults to False. If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Defaults to false.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.Gather Gather slices from params according to indices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.Gather  
tf.raw_ops.Gather(
    params, indices, validate_indices=True, name=None
)
 indices must be an integer tensor of any dimension (usually 0-D or 1-D). Produces an output tensor with shape indices.shape + params.shape[1:] where: # Scalar indices
output[:, ..., :] = params[indices, :, ... :]

# Vector indices
output[i, :, ..., :] = params[indices[i], :, ... :]

# Higher rank indices
output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
 If indices is a permutation and len(indices) == params.shape[0] then this operation will permute params accordingly. validate_indices: DEPRECATED. If this operation is assigned to CPU, values in indices are always validated to be within range. If assigned to GPU, out-of-bound indices result in safe but unspecified behavior, which may include raising an error.   
 


 Args
  params   A Tensor.  
  indices   A Tensor. Must be one of the following types: int32, int64.  
  validate_indices   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as params.
tf.raw_ops.GatherNd   Gather slices from params into a Tensor with shape specified by indices.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.GatherNd  
tf.raw_ops.GatherNd(
    params, indices, name=None
)
 indices is a K-dimensional integer tensor, best thought of as a (K-1)-dimensional tensor of indices into params, where each element defines a slice of params: output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]]
 Whereas in tf.gather indices defines slices into the axis dimension of params, in tf.gather_nd, indices defines slices into the first N dimensions of params, where N = indices.shape[-1]. The last dimension of indices can be at most the rank of params: indices.shape[-1] <= params.rank
 The last dimension of indices corresponds to elements (if indices.shape[-1] == params.rank) or slices (if indices.shape[-1] < params.rank) along dimension indices.shape[-1] of params. The output tensor has shape indices.shape[:-1] + params.shape[indices.shape[-1]:]
 Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, a 0 is stored in the corresponding output value. Some examples below. Simple indexing into a matrix: indices = [[0, 0], [1, 1]]
params = [['a', 'b'], ['c', 'd']]
output = ['a', 'd']
 Slice indexing into a matrix: indices = [[1], [0]]
params = [['a', 'b'], ['c', 'd']]
output = [['c', 'd'], ['a', 'b']]
 Indexing into a 3-tensor: indices = [[1]]
params = [[['a0', 'b0'], ['c0', 'd0']],
          [['a1', 'b1'], ['c1', 'd1']]]
output = [[['a1', 'b1'], ['c1', 'd1']]]


indices = [[0, 1], [1, 0]]
params = [[['a0', 'b0'], ['c0', 'd0']],
          [['a1', 'b1'], ['c1', 'd1']]]
output = [['c0', 'd0'], ['a1', 'b1']]


indices = [[0, 0, 1], [1, 0, 1]]
params = [[['a0', 'b0'], ['c0', 'd0']],
          [['a1', 'b1'], ['c1', 'd1']]]
output = ['b0', 'b1']
 Batched indexing into a matrix: indices = [[[0, 0]], [[0, 1]]]
params = [['a', 'b'], ['c', 'd']]
output = [['a'], ['b']]
 Batched slice indexing into a matrix: indices = [[[1]], [[0]]]
params = [['a', 'b'], ['c', 'd']]
output = [[['c', 'd']], [['a', 'b']]]
 Batched indexing into a 3-tensor: indices = [[[1]], [[0]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
          [['a1', 'b1'], ['c1', 'd1']]]
output = [[[['a1', 'b1'], ['c1', 'd1']]],
          [[['a0', 'b0'], ['c0', 'd0']]]]

indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
          [['a1', 'b1'], ['c1', 'd1']]]
output = [[['c0', 'd0'], ['a1', 'b1']],
          [['a0', 'b0'], ['c1', 'd1']]]


indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
          [['a1', 'b1'], ['c1', 'd1']]]
output = [['b0', 'b1'], ['d0', 'c1']]
 See also tf.gather and tf.batch_gather.
 


 Args
  params   A Tensor. The tensor from which to gather values.  
  indices   A Tensor. Must be one of the following types: int32, int64. Index tensor.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as params.